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EMNLP 2025 in Suzhou

Sun, 30 Nov 2025 00:00:00 +0000

EMNLP 2025 in Suzhou

Disclaimer: The research discussed in this post is entirely unrelated to my professional responsibilities. The work described here reflects independent academic activity that I pursue out of personal interest in modern machine learning research. All papers referenced below have been officially approved for publication and contain no proprietary or confidential information from my current employer. The material presented should not be interpreted as investment advice, financial analysis, or as having any applicability to my employer’s activities. Its relevance is strictly limited to the advancement of empirical methods and scientific understanding in machine learning.

Motivation

This year at EMNLP 2025 in Suzhou, my colleague Khaled Al Nuaimi and I attended the conference so that Khaled could present his paper on Evasive Answers in Financial Q&A, and also to explore current R&D trends in empirical NLP.

While walking through the poster sessions, we saw a dozen of papers closely related with our recent contributions and joint research program with Khalifa University, where I co-supervise the PhD work of my colleagues:

Across these doctoral projects, we are building a coherent, multi-layer research program at the intersection of:

Complex networks & complexity science (financial labour networks, institutional networks, contagion processes)
Feature importance & explainable AI (alignment between model predictions and explanations in LLMs & Graphs (Networks) ML models)
Multimodality: audio + text (tone, prosody, evasiveness, spoken Q&A behaviour)
Financial Q&A and behavioural analytics (linguistic deception, psychological discourse strategies, managerial behaviour)
Agent-based modelling (ABM) with explainable agents (replacing zero-intelligence coin-flipping agents with more realistic, behaviourally grounded AI agents)

At EMNLP 2025, we encountered many papers that map surprisingly well onto this agenda, in some cases reinforcing our assumptions, in others providing tools or methodologies we can directly adopt.

Below is a curated summary of the key papers we saw and how they connect to our ongoing work.

Relevant (to us) EMNLP 2025 Papers

Zero-Shot Extraction of Stock Relationship Graphs with LLMs (Zhou et al., FinNLP 2025)

paper

What the paper does:

Uses LLMs as zero-shot knowledge bases to extract rich, typed company–company relations (supplier, competitor, JV partner, investor…).
Builds multi-relational stock-market graphs from LLM outputs.
Trains GNNs (RGCN, RGAT) for stock return ranking, outperforming GICS/Wikidata graphs.

Connections to our research:

This work aligns most closely with Abdulla’s line of research. Beyond reconstructing networks of past co-employment directly from SFC data, we could extend the financial ecosystem by using LLMs to infer missing inter-institution relationships. For example: competition, parent–subsidiary structures, shared ownership, or other strategic ties.

Adding these extra edges would enrich Abdulla’s labour-contagion models by capturing sub-industry exposure, competitive pressures, organizational linkages, and cross-firm risk propagation. In short, it gives us a more complete picture of how information, shocks, or turnover might spread across the ecosystem.

From the perspective of our agent-based modelling (ABM) vision, a multi-relational LLM-inferred graph provides a richer structural layer for agents to operate on. Employee-agents could factor in market competition or firm relations when deciding whether to move to a new opportunity, while firm-agents could incorporate similar relational cues when adjusting headcount or assessing talent flows. This creates a more behaviourally realistic and environmentally aware agent society than classic zero-intelligence ABMs.

Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey (Chih-Kai Yang et al.)

paper

What the paper does:

A taxonomy for evaluating audio-language models (LALMs), organized along four axes:

Auditory awareness & processing
Knowledge & reasoning
Dialogue-oriented ability
Fairness, safety & trustworthiness

It also highlights several gaps in current audio-NLP practice:

No clean separation between content and paralinguistic cues
Weak robustness evaluation (noise, speaker variation, accents)
Limited cultural/linguistic diversity in benchmarks
Limited evaluation of dialogue quality and response appropriateness

Connections to our research:

Hamdan’s first research contribution: Residual Speech Embeddings

A natural question is: What differentiates LLMs from LALMs? In principle, LALMs should integrate auditory information in addition to text: capturing tone, speaker traits, emotional signals, and other paralinguistic cues that are lost in transcripts.

However, the survey highlights a consistent limitation: current LALMs exhibit very limited auditory awareness. They often fail to detect speaker changes, emotional shifts, or mismatches between lexical content and paralinguistic delivery. In practice, their behaviour is much closer to text-only LLMs than to genuine audio-language models.

These observations align closely with what we have seen empirically. This is precisely the motivation for Hamdan’s work on Residual Speech Embeddings, where we demonstrate that self-supervised speech models tend to over-emphasize lexical information at the expense of paralinguistic cues.

Our approach explicitly removes the lexical component from the speech embedding space, leaving a representation that captures tone and other non-textual characteristics. Empirically, we show that this improves performance on tone-classification tasks, confirming that isolating the paralinguistic signal is beneficial.

For reference:

This EMNLP paper essentially validates the direction of our work: the field currently lacks benchmarks that disentangle content from paralinguistic cues, and residual speech embeddings provide a practical step toward filling that gap.

It is also worth noting that Hamdan’s work naturally connects to Khaled’s research on financial Q&A. One of Khaled’s recent contributions focuses on detecting evasive answers in earnings calls and FOMC press conferences, based purely on the textual transcripts. A logical next step is to extend this analysis to the audio recordings themselves.

Working directly from audio opens new questions that transcripts cannot capture:

Are there identifiable paralinguistic cues associated with evasive answers?
Do these cues vary depending on the type of evasive tactic employed?

Hamdan’s residual embeddings provide exactly the kind of representation needed to explore these audio dimensions of evasiveness in a controlled and interpretable way.

From a broader perspective, this line of work also hints at a longer-term, more speculative direction for our agent-based modelling (ABM) research. If we can reliably disentangle and quantify paralinguistic cues, it becomes conceivable—though certainly not immediate—to develop speech-enabled agents whose behaviour is influenced by vocal signals such as stress, confidence, or hesitation. While this is not a short-term objective, it illustrates how multimodal modelling could eventually lead to more realistic simulations of financial decision-making dynamics.

The Medium Is Not the Message: Deconfounding Document Embeddings via Linear Concept Erasure (Fan et al.)

paper

What the paper does:

Embedding-based similarity metrics between text sequences can be influenced not just by the content dimensions we most care about, but can also be biased by spurious attributes like the text’s source or language. These document confounders cause problems for many applications, but especially those that need to pool texts from different corpora.

To mitigate this, the authors introduce Linear Concept Erasure (LCE), a linear projection method that identifies the subspace corresponding to these confounders and removes it, while preserving the semantic core of the representation. Their main findings are:

cross-corpus clustering and retrieval improve after removing the confounder subspace;
downstream semantic tasks remain essentially unchanged, which is the expected outcome (semantics are preserved, and only nuisance variation is removed);
the method is simple and computationally light.

In practice, this is a form of representation surgery: identify the part of the embedding space capturing the unwanted factor, project it out, and keep the rest.

Connections to our research:

This paper reinforces an intuition that also underpins Hamdan’s work on Residual Speech Embeddings: To clean the representation space by removing unwanted components.

There is a clear structural similarity between the two approaches:

Residual speech embeddings remove the linear component of speech that can be predicted from text, isolating paralinguistic information;
LCE removes the linear component of text embeddings that corresponds to medium/domain/style, isolating semantic information.

In both cases, the idea is the same:

Identify a linear subspace corresponding to an unwanted factor, remove it, and work with the cleaned embedding.

We used the term residual embedding simply by analogy with “residual returns” in quantitative finance, where regression residuals are often used to remove unwanted exposures. LCE formalizes a closely related idea on the text side, but using discriminative subspace estimation rather than cross-modal regression.

This opens several possibilities relevant to our research program:

removing speaker identity or channel artifacts from audio embeddings;
removing demographic confounders in textual or multimodal representations (cf. our name-based demographics study);
isolating emotional or deceptive cues in financial Q&A;
making embeddings cross-corpus comparable by suppressing stylistic and format-specific variation across sources like broker reports, earnings calls, and regulatory filings.

Overall, LCE provides an elegant confirmation that linear subspace removal, the core idea behind Hamdan’s residual embeddings, is a powerful and general approach for disentangling competing factors in representation spaces.

Finally, This echoes an intuition I had years ago when doing topic modelling on business descriptions: the dominant axis of variation was almost always the industry sector, which is obvious but not very informative. Removing that first dimension (for example by residualizing to sector embeddings) may be necessary to reveal more interesting structure.

CafGa: Customizing Feature Attributions to Explain Language Models (Boyle et al.)

paper

What the paper does:

CaFGa proposes an attribution framework that lets users control the granularity of explanations for LLM decisions. Instead of relying on token-level heatmaps, typically noisy, unstable, and expensive, CaFGa groups text into interpretable segments (sentences, clauses, rhetorical units) and computes attributions at this coarser, more meaningful level.

A key component is the use of perturbation–fidelity curves to assess whether the explanation is faithful to the model’s actual decision process. The idea is simple: remove or mask the high-attribution segments and measure how the prediction changes. Faithful explanations yield steep fidelity curves; unfaithful ones remain flat.

The authors show that coarse-grained explanations tend to be more stable, more readable, and more faithful to model behavior than traditional word-level attributions.

Connections to our research:

Although Saeed’s credit-risk classifier operates on tabular features rather than text, CaFGa suggests a direct methodological analogue: Group features into semantic blocks (employment, income, liabilities, credit history) and compute block-level attributions. Perturbation–fidelity curves would then allow us to test whether the explanation aligns with the model’s true decision boundary. This complements our ongoing work on explanation alignment, where we repeatedly observe that LLM-generated natural-language explanations diverge from the model’s actual reasoning. CaFGa provides a principled way to quantify this divergence.

The paper is also highly relevant to Khaled’s work on evasive answers in financial Q&A. CEO (and CFO/COO) responses in earnings calls naturally decompose into meaningful discourse segments: direct answers, hedges, topic shifts, credibility boosters, vague qualifiers, and so on. CaFGa’s segment-level attribution framework fits this structure almost perfectly. It would allow us to quantify which parts of an answer contribute most to the model’s evasiveness prediction, and whether those attributions are faithful. This could naturally lead to a follow-up study on segment-level explanations for evasive answer detection.

Beyond academic value, such a tool would be practically useful for financial analysts during live Q&A sessions or one-to-one calls with management. Highlighting the segments most responsible for an “evasive” classification, together with an indication of in which direction the answer is evasive (topic shift, hedging, excessive reassurance, lack of specificity), could help analysts decide when to push further, and on which aspect of the response to follow up.

LLMs Don’t Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations (Mayne et al.)

paper

What the paper does:

This paper investigates whether LLMs understand their own decision boundaries when used as classifiers. The authors evaluate a simple but revealing setup: small tabular datasets (2–4 features) are converted into natural-language profiles, and the LLM is asked to (i) make a prediction and then (ii) provide a self-generated counterfactual explanation (SCE): “What is the smallest change to this profile that would flip your decision?”

Crucially, authors can enumerate the full input space, so they know exactly where the decision boundary lies.

They evaluate two properties:

Validity: Does the counterfactual actually flip the model’s prediction?
Minimality: Is the suggested change close to the true decision boundary?

The core findings:

When asked for any counterfactual, LLMs often produce valid but highly non-minimal edits (overshooting the boundary).
When asked for the smallest change, LLMs produce minimal-looking edits that frequently fail to flip the prediction.
No current model (including GPT-4.1, o3, Claude Sonnet 3.7) achieves both validity and minimality at the same time.

In practice: LLMs generate counterfactuals that sound correct but fail to match the true decision boundary.

Connections to our research:

This paper directly reinforces the findings from Saeed’s paper on credit-risk explanation alignment, where we show that LLM-generated explanations for credit decisions often diverge from the factors that actually drive the model’s classification. That work focused on feature-importance misalignment; this paper adds a complementary phenomenon: boundary-awareness misalignment.

Together these papers paint a consistent picture:

LLMs struggle to articulate why they made a decision (feature attributions).
LLMs also struggle to articulate how to change the decision (counterfactuals).

The methodology in this poster offers a clean way to test this in our credit-risk setting:

Use the same borrower profiles and prompt templates from Saeed’s work.
Ask the LLM to predict default vs. non-default.
Request a self-generated counterfactual: “What is the minimal change that would flip your decision?”
Parse the edited profile back to tabular form.
Re-query the model and measure validity + minimality.

Can we replicate this research in our setting?

While a direct replication of their protocol in the credit domain is rather straightforward, the more interesting direction for us is constructive: Can we help LLMs generate counterfactuals that are both valid and minimal?

This suggests a methodological extension: Adapting classical ML counterfactual engines (e.g., DiCE) on LightGBM) to identify truly minimal edits… This could lead to a more ambitious follow-up paper: “Teaching (Credit) LLMs Their Own Decision Boundaries” where the goal is not only to expose the misalignment but to improve boundary-awareness using hybrid ML–LLM pipelines (tentatively).

Conclusion

This quick wrap-up covers only a handful of the papers we came across, but they were among the most directly connected to the research lines pursued by the PhD students I’m co-supervising. Each of them sharpens or extends the work we’ve already been doing, whether on representations, audio–text multimodality, explainability, or financial Q&A behaviour.

I’m writing this just a few hours before boarding my flight to NeurIPS (San Diego). With a bit of luck, I’ll come back with another batch of papers that are equally interesting and aligned with our research program.

Serious, Sassy, or Sad? Teaching Machines to Read the Room (From Speech Embeddings)

Sun, 16 Feb 2025 00:00:00 +0000

Serious, Sassy, or Sad? Teaching Machines to Read the Room (From Speech Embeddings)

📌 A politician says ‘We are fully in control of the situation’ — but their voice is trembling. Would you trust the words or the tone? Can AI detect when speech and vocal tone don’t match?

Introduction
The experiment
Research question
tl;dr
Loading the Embeddings
Understanding the Embeddings
Implications
Validating the Findings with Supervised Classification
Short vs. Long Sentences
🔍 Key Takeaways
Discrepancy Index – A Future Research Direction

N.B. This blog post serves as a pedagogical introduction to the core concepts and methodologies explored in Hamdan Al Ahbabi’s PhD research as part of his doctoral studies at Khalifa University, under my co-supervision. It is entirely non-work-related and should not be interpreted as such. Instead, its purpose is to support and complement future presentations at academic conferences.

Introduction

In our previous blog post, Disentangling Speech Embeddings, we introduced a simple yet effective method for removing linguistic content (what is being said) from speech embeddings. Concretely, we trained a linear model to predict raw speech embeddings from the corresponding text embeddings and used the residuals (differences) as a proxy for non-linguistic vocal features. We demonstrated that the resulting “residual” embeddings retained enough vocal cue information to cluster sentences by speaker identity rather than semantic content — suggesting that these embeddings preserve vocal tone and style independently of the words spoken.

Now, we take this idea one step further. This time, we explore whether these residual embeddings can help classify how something is said—focusing on vocal tone and speaking style.

The experiment

We use a single speaker (a female, US English voice - Natalie from murf.ai) and have her deliver different sentences across a variety of tones and styles. The sentences range from neutral business jargon to casual, sentiment-laden phrases (both positive and negative). The 12 distinct tones/styles tested include: Sorrowful, Inspirational, Terrified, Furious, Newscast Casual, Conversational, Angry, Sad, Meditative, Newscast Formal, Narration, and Promo.

Research question

👉 Is it easier to classify speech into one of these 12 tones/styles using the original wav2vec2 audio embeddings, or does the “residual” version (where content has been removed) provide a cleaner signal for tone classification?

tl;dr

✅ Both linear and non-linear models perform better using the residual embeddings than the raw audio ones, where the actual spoken content appears to confuse the model.

✅ Linear and non-linear classifiers perform similarly, suggesting that the relevant information (tone/style) is well-captured in a way that can be extracted with simple linear methods.

✅ Shorter sentences provide fewer vocal cues, making tone classification harder. Residual embeddings help the most in these cases by removing content interference.

✅ With longer sentences, tone information is naturally stronger, reducing the need for content filtering—though residual embeddings still offer a slight edge.

✅ This supports the idea that removing linguistic content enhances models’ ability to focus on paralinguistic features (tone, style, speaker characteristics).

Loading the Embeddings

Let’s start by loading the data (sentences and embeddings).

import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import Ridge, LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import (
    accuracy_score, f1_score, roc_auc_score, confusion_matrix)
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE


styles = [
    "Sorrowful", "Inspirational", "Terrified",
    "Furious", "Newscast Casual", "Conversational",
    "Angry", "Sad", "Meditative",
    "Newscast Formal", "Narration", "Promo",
]


sentences = pd.read_parquet("earnings_calls_sentences.parquet")
positive_sentences = pd.read_parquet("positive_sentences.parquet")
negative_sentences = pd.read_parquet("negative_sentences.parquet")

display(sentences.sample(5))
display(positive_sentences.sample(5))
display(negative_sentences.sample(5))

	sentence
5	For the next 100 years to come, we will contin...
36	It's not just that we recruit the best or that...
13	You see the overseas sales ratio of the in-hou...
46	Compared to the IOCs over the last five years,...
31	Not just profitable growth for ExxonMobil, but...

	sentence
25	The resilience and optimism you maintain in th...
3	That meal was incredibly delicious.
10	Your kindness and generosity are truly inspiring.
18	Everything is going so well—I feel incredibly ...
4	I'm so proud of you for achieving your goal!

	sentence
15	I couldn't be more frustrated with how things ...
8	You are the most unreliable person I've ever met.
37	It is disheartening to see that despite having...
5	You always find a way to make things worse.
1	I absolutely hate dealing with this kind of si...

df_audio_emb = pd.read_parquet("wav2vec2_audio_embeddings.parquet")
df_text_emb = pd.read_parquet("dataframe_text_embeddings.parquet")

# Convert DataFrames to NumPy arrays
X = df_text_emb.values    # shape: (n_samples, 1536)
Y = df_audio_emb.values   # shape: (n_samples, 768)

print("Text embeddings shape:", X.shape)
print("Audio embeddings shape:", Y.shape)

Text embeddings shape: (1584, 1536)
Audio embeddings shape: (1584, 768)

# Train Ridge regression model
def train_ridge_regression(X, Y, alpha=1.0):
    model = Ridge(alpha=alpha)
    model.fit(X, Y)
    return model

model = train_ridge_regression(X, Y)

# Compute residual embeddings
Y_pred = model.predict(X)
E = Y - Y_pred

df_residual_emb = pd.DataFrame(E, index=df_audio_emb.index)

df_residual_emb

	0	1	2	3	4	5	6	7	8	9	...	758	759	760	761	762	763	764	765	766	767
sent_speaker
embeddings_downloaded_audio_00_Angry.npy	0.000547	-0.005123	0.033253	-0.038299	0.013721	-0.038447	0.007130	0.023777	0.000756	-0.018350	...	-0.011541	-0.012827	0.026055	-0.032220	-0.038547	0.006204	-0.010762	0.002884	-0.038804	0.044158
embeddings_downloaded_audio_00_Conversational.npy	-0.012998	0.003234	0.010352	-0.004429	0.019514	-0.000994	0.014857	0.005752	-0.004121	-0.029278	...	-0.032924	0.002646	0.021952	0.001924	-0.037578	-0.013107	-0.020910	-0.005894	0.017660	-0.035358
embeddings_downloaded_audio_00_Furious.npy	-0.066253	0.015863	-0.018699	0.040799	-0.021040	0.018685	0.038205	-0.000754	0.012815	-0.008809	...	0.003965	0.008530	0.002404	-0.032704	0.055339	-0.023414	-0.019636	0.023885	-0.000745	-0.023359
embeddings_downloaded_audio_00_Inspirational.npy	-0.002424	-0.028308	0.000459	0.002153	-0.007286	0.022152	-0.025893	-0.025777	-0.058059	-0.007379	...	0.016637	-0.011735	0.017605	0.017890	0.033506	0.005326	-0.003464	-0.004350	-0.007208	0.026986
embeddings_downloaded_audio_00_Meditative.npy	0.001378	-0.011582	0.036844	-0.042164	-0.036087	0.010559	-0.005657	0.025860	-0.070218	0.003857	...	0.046636	0.004565	0.011343	-0.003232	-0.006497	-0.026536	0.002865	-0.012032	-0.005313	0.042849
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
embeddings_positive_text_downloaded_audio_39_Newscast Formal.npy	-0.010689	-0.001865	-0.040611	0.009453	0.047540	-0.027685	0.015540	0.012837	0.010790	0.011269	...	-0.015807	-0.010005	-0.005689	-0.000988	-0.033687	0.001929	0.008778	-0.015480	-0.008912	-0.012508
embeddings_positive_text_downloaded_audio_39_Promo.npy	-0.035135	0.026699	-0.025716	-0.008928	0.055495	0.066652	0.038595	-0.007269	-0.032083	-0.002548	...	0.044913	0.003458	0.005861	-0.050246	0.004993	0.000369	0.010904	-0.010641	-0.021717	-0.054168
embeddings_positive_text_downloaded_audio_39_Sad.npy	0.019876	0.014143	-0.087296	0.017254	0.075202	-0.049448	-0.006937	-0.039578	0.067603	-0.001711	...	-0.036482	-0.010577	-0.000425	0.051968	-0.067635	0.016389	-0.007544	-0.042562	0.022262	-0.001306
embeddings_positive_text_downloaded_audio_39_Sorrowful.npy	0.021177	-0.034878	-0.025202	0.025799	-0.004771	-0.026035	-0.018924	-0.022863	0.038629	-0.001309	...	-0.024871	0.010005	0.003231	0.040676	0.012732	-0.019660	0.016285	-0.011352	0.044231	0.067834
embeddings_positive_text_downloaded_audio_39_Terrified.npy	0.019007	-0.010457	0.005947	0.026304	-0.048532	0.004886	-0.005772	-0.014250	0.044395	0.002009	...	0.006794	0.008677	0.002136	0.020690	0.000381	-0.016183	0.008968	0.016245	0.035921	0.059694

1584 rows × 768 columns

Understanding the Embeddings

To begin, we visualize the embeddings by projecting them into a 2D space using standard dimensionality reduction techniques like PCA and t-SNE. This allows us to explore whether any clear structure emerges in these representations.

X_text = df_text_emb.values
X_audio = df_audio_emb.values
X_resid = df_residual_emb.values

# Extract labels: assuming styles are encoded in filenames
labels = pd.Series([elem.split('_')[-1].split('.npy')[0]
                    for elem in df_audio_emb.index])

def plot_pca_tsne(X, labels, title_prefix):
    """
    Apply PCA and t-SNE to reduce dimensions and visualize embeddings.
    """
    # Convert labels to categorical numeric values
    label_values, label_mapping = pd.factorize(labels)

    # Apply PCA
    pca = PCA(n_components=2)
    X_pca = pca.fit_transform(X)

    # Apply t-SNE
    tsne = TSNE(n_components=2, perplexity=5, random_state=42)
    X_tsne = tsne.fit_transform(X)

    fig, axes = plt.subplots(1, 2, figsize=(14, 6))

    # PCA Plot
    scatter = axes[0].scatter(
        X_pca[:, 0], X_pca[:, 1],
        c=label_values, cmap="tab20", alpha=0.7)
    axes[0].set_title(f"{title_prefix} - PCA Projection")
    axes[0].set_xlabel("PC1")
    axes[0].set_ylabel("PC2")

    # t-SNE Plot
    scatter = axes[1].scatter(
        X_tsne[:, 0], X_tsne[:, 1],
        c=label_values, cmap="tab20", alpha=0.7)
    axes[1].set_title(f"{title_prefix} - t-SNE Projection")
    axes[1].set_xlabel("t-SNE Dim 1")
    axes[1].set_ylabel("t-SNE Dim 2")

    # Add legend for the unique labels
    legend_labels = {i: label for i, label in enumerate(label_mapping)}
    handles = scatter.legend_elements()[0]
    labels_list = [legend_labels[i] for i in range(len(handles))]
    axes[1].legend(handles, labels_list,
                   loc="best", fontsize="small", title="Styles")

    plt.show()


    
# Run PCA & t-SNE for Text Embeddings
content_labels = pd.Series(
    ["business"] * 52 * 12 + ["negative"] * 40 * 12 + ["positive"] * 40 * 12)
plot_pca_tsne(X_text, content_labels, "Text Embeddings")

# Run PCA & t-SNE for Audio Embeddings
plot_pca_tsne(X_audio, labels, "Audio Embeddings")

# Run PCA & t-SNE for Residual Embeddings
plot_pca_tsne(X_resid, labels, "Residual Embeddings")

These visualizations confirm that residual embeddings provide clearer clustering by tone, reinforcing their usefulness in capturing vocal style.

In more details:

Text Embeddings

The first row displays the text embeddings projected onto a two-dimensional space using PCA (left) and t-SNE (right). Since these embeddings represent only the textual content and not the way sentences are spoken, the different tone/style labels do not appear distinct in the PCA projection. Instead, we only observe clusters corresponding to:

Business-related sentences (52 samples)
Positive sentiment sentences (40 samples)
Negative sentiment sentences (40 samples)

This indicates that text embeddings primarily encode content (and sentiment).

Audio Embeddings

For the raw audio embeddings, the PCA projection (center-left) does not exhibit clear structure. While a handful of outliers are present, the majority of the data points are densely packed without clear separation. This suggests that in their raw form, audio embeddings mix linguistic content and vocal tone/style, making them difficult to disentangle.

However, when applying t-SNE (center-right), we start to observe some degree of clustering by tone/style. Notably:

The “Promo” (grey) and “Furious” (orange) styles form noticeable clusters.
Other styles remain partially mixed, indicating that raw audio embeddings do encode tone information but not in a way that is easily separable.

These findings reinforce the idea that raw speech embeddings contain a blend of both linguistic and speaker-specific information, making it challenging to extract tone/style characteristics directly.

Residual Embeddings

The residual embeddings, which remove linguistic content using the elementary method presented in Disentangling Speech Embeddings, exhibit a much stronger clustering structure:

PCA projection (bottom-left) shows better-defined clusters (e.g., blue, orange, brown, grey) compared to raw audio embeddings, although the separation is not perfect.
t-SNE projection (bottom-right) reveals a remarkably clear separation of the 12 tones/styles. Unlike raw audio embeddings, the residual embeddings distinctly group sentences by how they are spoken rather than what is being said.

This result strongly suggests that removing linguistic content leaves behind a representation that is highly predictive of tone and style. In other words:

Text embeddings capture only the content of speech (what is being said).
Residual embeddings predominantly encode vocal tone/style (how it is being said).
Raw audio embeddings contain a mixture of both, making classification more difficult.

Implications

If the goal is tone/style classification, residual embeddings should be used instead of raw audio embeddings.
Residual embeddings cluster strongly by style, meaning even simple models (e.g., logistic regression) should be able to classify tone effectively.
This experiment confirms that an elementary linear disentanglement technique successfully separates tone and linguistic content in speech embeddings.

Validating the Findings with Supervised Classification

So far, our unsupervised analysis has shown that residual embeddings exhibit clearer clustering by tone/style compared to raw audio embeddings. But how well does this separation translate to practical classification performance?

To test this, we move to a supervised classification approach:

🔹 Objective: Train a classifier to predict tone/style from speech embeddings and compare performance across different representations (audio vs. residual).

🔹 Hypothesis: If residual embeddings truly capture tone/style better than raw audio embeddings, a simple classifier should achieve higher accuracy on residuals than on raw audio embeddings.

🔹 Experiment:

Train a logistic regression model and a random forest classifier on both raw audio and residual embeddings.
Compare accuracy, F1-score, and AUC-ROC across models and representations.
Evaluate whether removing linguistic content improves the model’s ability to focus on tone and style.

Let’s dive into the code and results:

# Function to train, evaluate, and return results
def evaluate_model(X, y, model, model_name):
    """Trains a model, evaluates performance, and returns metrics."""
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42)
    
    # Train model
    model.fit(X_train, y_train)
    
    # Predict
    y_pred = model.predict(X_test)
    y_proba = (
        model.predict_proba(X_test)
        if hasattr(model, "predict_proba") else None
    )
    
    # Compute Metrics
    acc = accuracy_score(y_test, y_pred)
    f1 = f1_score(y_test, y_pred, average="weighted")
    roc_auc = (
        roc_auc_score(y_test, y_proba, multi_class="ovr")
        if y_proba is not None else None
    )

    y_pred = model.predict(X)
    conf_matrix = confusion_matrix(y, y_pred)
    
    return {"Model": model_name,
            "Accuracy": acc,
            "F1 Score": f1,
            "AUC-ROC": roc_auc,
            "Confusion Matrix": conf_matrix}


# Function to display confusion matrix
def plot_confusion_matrix(conf_matrix, title):
    """Plots a confusion matrix with heatmap."""
    plt.figure(figsize=(6, 5))
    sns.heatmap(conf_matrix, annot=True, fmt="d", cmap="Blues")
    plt.xlabel("Predicted Label")
    plt.ylabel("True Label")
    plt.title(title)
    plt.show()

# Prepare data for classification tasks
def prepare_classification_data(
    df_audio_emb, df_residual_emb, df_text_emb, use_case="business"):
    if use_case == "business":
        index = (
            ~(df_audio_emb.index.str.contains("positive")) &
            ~(df_audio_emb.index.str.contains("negative")))
    elif use_case == "sentimental_text":
        index = (
            (df_audio_emb.index.str.contains("positive")) |
            (df_audio_emb.index.str.contains("negative")))
    elif use_case == "short_text":
        mask = (
            df_audio_emb.index.str.extract(r'_(\d+)_')[0]
            .astype(float)
            .between(0, 19)
        )
        mask.index = df_audio_emb.index
        index = (
            (df_audio_emb.index.str.contains("positive") |
             df_audio_emb.index.str.contains("negative")) &
            mask
        ).tolist()
    elif use_case == "long_text":
        mask = (
            df_audio_emb.index.str.extract(r'_(\d+)_')[0]
            .astype(float)
            .between(20, 39)
        )
        mask.index = df_audio_emb.index
        index = (
            (df_audio_emb.index.str.contains("positive") |
             df_audio_emb.index.str.contains("negative")) &
            mask
        ).tolist()       
    
    X_audio = df_audio_emb[index]
    y_audio = [elem.split('_')[-1].split('.npy')[0]
               for elem in X_audio.index]
    
    X_resid = df_residual_emb[index]
    y_resid = [elem.split('_')[-1].split('.npy')[0]
               for elem in X_resid.index]
    
    X_text = df_text_emb[index]
    y_text = y_audio
    
    return (X_audio, y_audio,
            X_resid, y_resid,
            X_text, y_text)

X_audio, y_audio, X_resid, y_resid, X_text, y_text = (
    prepare_classification_data(
        df_audio_emb, df_residual_emb, df_text_emb,
        use_case="business"))


# Convert labels to numerical format
unique_labels = sorted(set(y_audio))
label_mapping = {label: idx for idx, label in enumerate(unique_labels)}
y_audio = np.array([label_mapping[label] for label in y_audio])
y_resid = np.array([label_mapping[label] for label in y_resid])
y_text  = np.array([label_mapping[label] for label in y_text])

# Convert DataFrames to numpy arrays
X_audio = X_audio.to_numpy()
X_resid = X_resid.to_numpy()
X_text  = X_text.to_numpy()

# Define models
log_reg = LogisticRegression(max_iter=1000, random_state=42)
random_forest = RandomForestClassifier(n_estimators=100, random_state=42)

# Evaluate models
results = []
results.append(
    evaluate_model(X_text, y_text, log_reg, "Logistic Regression (X_text)"))
results.append(
    evaluate_model(X_audio, y_audio, log_reg, "Logistic Regression (X_audio)"))
results.append(
    evaluate_model(X_resid, y_resid, log_reg, "Logistic Regression (X_resid)"))
results.append(
    evaluate_model(X_text, y_text, random_forest, "Random Forest (X_text)"))
results.append(
    evaluate_model(X_audio, y_audio, random_forest, "Random Forest (X_audio)"))
results.append(
    evaluate_model(X_resid, y_resid, random_forest, "Random Forest (X_resid)"))

# Convert results to DataFrame
results_df = pd.DataFrame(results).drop(columns=["Confusion Matrix"])

print("\nClassification Results:")
display(results_df)

Classification Results:

	Model	Accuracy	F1 Score	AUC-ROC
0	Logistic Regression (X_text)	0.000	0.000000	0.096726
1	Logistic Regression (X_audio)	0.888	0.889941	0.990831
2	Logistic Regression (X_resid)	0.944	0.944745	0.997317
3	Random Forest (X_text)	0.000	0.000000	0.095351
4	Random Forest (X_audio)	0.800	0.797580	0.972571
5	Random Forest (X_resid)	0.920	0.919786	0.996321

As anticipated, text embeddings do not capture tone or style information, as they solely encode linguistic content. We included them in the experiment as a control to validate this assumption, and the results confirm that tone cannot be recovered from text embeddings alone. This is clearly reflected in the classification performance shown in the bar plot below:

# Plot classification results
plt.figure(figsize=(10, 5))
results_df.set_index("Model").plot(kind="bar", figsize=(10, 5))
plt.title("Classification Performance Comparison", fontsize=14)
plt.ylabel("Score", fontsize=12)
plt.xticks(rotation=45, fontsize=10)
plt.yticks(fontsize=10)
plt.legend(loc="lower right", fontsize=10)
plt.grid(True, linestyle="--", linewidth=0.5, alpha=0.7)
plt.show()

Remark: Even with simple models like logistic regression, residual embeddings achieved over 94% accuracy. This suggests that tone information is not only well-preserved but also highly predictable once linguistic content is removed. In contrast, raw audio embeddings performed worse, likely because they mix content with tone.

X_audio, y_audio, X_resid, y_resid, X_text, y_text = (
    prepare_classification_data(
        df_audio_emb, df_residual_emb, df_text_emb,
        use_case="sentimental_text"))


# Convert labels to numerical format
unique_labels = sorted(set(y_audio))
label_mapping = {label: idx for idx, label in enumerate(unique_labels)}
y_audio = np.array([label_mapping[label] for label in y_audio])
y_resid = np.array([label_mapping[label] for label in y_resid])
y_text  = np.array([label_mapping[label] for label in y_text])

# Convert DataFrames to numpy arrays
X_audio = X_audio.to_numpy()
X_resid = X_resid.to_numpy()
X_text  = X_text.to_numpy()

# Define models
log_reg = LogisticRegression(max_iter=1000, random_state=42)
random_forest = RandomForestClassifier(n_estimators=100, random_state=42)

# Evaluate models
results = []
results.append(
    evaluate_model(X_text, y_text, log_reg, "Logistic Regression (X_text)"))
results.append(
    evaluate_model(X_audio, y_audio, log_reg, "Logistic Regression (X_audio)"))
results.append(
    evaluate_model(X_resid, y_resid, log_reg, "Logistic Regression (X_resid)"))
results.append(
    evaluate_model(X_text, y_text, random_forest, "Random Forest (X_text)"))
results.append(
    evaluate_model(X_audio, y_audio, random_forest, "Random Forest (X_audio)"))
results.append(
    evaluate_model(X_resid, y_resid, random_forest, "Random Forest (X_resid)"))


# Convert results to DataFrame
results_df = pd.DataFrame(results).drop(columns=["Confusion Matrix"])

print("\nClassification Results:")
display(results_df)

Classification Results:

	Model	Accuracy	F1 Score	AUC-ROC
0	Logistic Regression (X_text)	0.000000	0.000000	0.120661
1	Logistic Regression (X_audio)	0.875000	0.873449	0.978296
2	Logistic Regression (X_resid)	0.880208	0.878072	0.986610
3	Random Forest (X_text)	0.000000	0.000000	0.097720
4	Random Forest (X_audio)	0.687500	0.682460	0.943511
5	Random Forest (X_resid)	0.848958	0.846386	0.975307

Logistic Regression performs surprisingly well on raw audio embeddings, likely because some tone information is already linearly separable, as suggested by the earlier 2D projections. However, Random Forests show a much stronger improvement with residual embeddings, benefiting from the removal of linguistic content. By eliminating this noise, residual embeddings provide a cleaner representation of paralinguistic features, making them more suitable for decision-tree-based learning.

# Plot classification results
plt.figure(figsize=(10, 5))
results_df.set_index("Model").plot(kind="bar", figsize=(10, 5))
plt.title("Classification Performance Comparison", fontsize=14)
plt.ylabel("Score", fontsize=12)
plt.xticks(rotation=45, fontsize=10)
plt.yticks(fontsize=10)
plt.legend(loc="lower right", fontsize=10)
plt.grid(True, linestyle="--", linewidth=0.5, alpha=0.7)
plt.show()

Note: Before expanding the dataset with longer sentences, we initially ran this experiment using only 20 short positive and 20 short negative sentences. In that setup, logistic regression on raw audio embeddings achieved 0.71 accuracy, while residual embeddings improved performance to 0.82. This suggests that for shorter sentences, where vocal cues are more limited or entangled with linguistic content, residual embeddings provide a clearer benefit. By stripping away the linguistic information, they make it easier for the model to focus on paralinguistic features, leading to a more pronounced lift in classification accuracy.

This motivated us to examine whether longer, more expressive sentences improve classification performance overall and to assess whether the advantage of residual embeddings over raw audio embeddings diminishes as more vocal cues become available.

Short vs. Long Sentences

To explore this, we compare model performance on short versus long sentences, starting with the short sentence case.

The short sentences case:

X_audio, y_audio, X_resid, y_resid, X_text, y_text = (
    prepare_classification_data(
        df_audio_emb, df_residual_emb, df_text_emb,
        use_case="short_text"))


# Convert labels to numerical format
unique_labels = sorted(set(y_audio))
label_mapping = {label: idx for idx, label in enumerate(unique_labels)}
y_audio = np.array([label_mapping[label] for label in y_audio])
y_resid = np.array([label_mapping[label] for label in y_resid])
y_text  = np.array([label_mapping[label] for label in y_text])

# Convert DataFrames to numpy arrays
X_audio = X_audio.to_numpy()
X_resid = X_resid.to_numpy()
X_text  = X_text.to_numpy()

# Define models
log_reg = LogisticRegression(max_iter=1000, random_state=42)
random_forest = RandomForestClassifier(n_estimators=100, random_state=42)

# Evaluate models
results = []
results.append(
    evaluate_model(X_text, y_text, log_reg, "Logistic Regression (X_text)"))
results.append(
    evaluate_model(X_audio, y_audio, log_reg, "Logistic Regression (X_audio)"))
results.append(
    evaluate_model(X_resid, y_resid, log_reg, "Logistic Regression (X_resid)"))
results.append(
    evaluate_model(X_text, y_text, random_forest, "Random Forest (X_text)"))
results.append(
    evaluate_model(X_audio, y_audio, random_forest, "Random Forest (X_audio)"))
results.append(
    evaluate_model(X_resid, y_resid, random_forest, "Random Forest (X_resid)"))


# Convert results to DataFrame
results_df = pd.DataFrame(results).drop(columns=["Confusion Matrix"])

print("\nClassification Results:")
display(results_df)

# Plot classification results
plt.figure(figsize=(10, 5))
results_df.set_index("Model").plot(kind="bar", figsize=(10, 5))
plt.title("Classification Performance Comparison", fontsize=14)
plt.ylabel("Score", fontsize=12)
plt.xticks(rotation=45, fontsize=10)
plt.yticks(fontsize=10)
plt.legend(loc="lower right", fontsize=10)
plt.grid(True, linestyle="--", linewidth=0.5, alpha=0.7)
plt.show()

Classification Results:

	Model	Accuracy	F1 Score	AUC-ROC
0	Logistic Regression (X_text)	0.000000	0.000000	0.121146
1	Logistic Regression (X_audio)	0.708333	0.700649	0.936023
2	Logistic Regression (X_resid)	0.822917	0.817159	0.978280
3	Random Forest (X_text)	0.000000	0.000000	0.093249
4	Random Forest (X_audio)	0.552083	0.533623	0.847435
5	Random Forest (X_resid)	0.697917	0.685180	0.947323

The long sentences case:

We now turn to longer sentences, where more vocal cues are naturally present.

X_audio, y_audio, X_resid, y_resid, X_text, y_text = (
    prepare_classification_data(
        df_audio_emb, df_residual_emb, df_text_emb,
        use_case="long_text"))


# Convert labels to numerical format
unique_labels = sorted(set(y_audio))
label_mapping = {label: idx for idx, label in enumerate(unique_labels)}
y_audio = np.array([label_mapping[label] for label in y_audio])
y_resid = np.array([label_mapping[label] for label in y_resid])
y_text  = np.array([label_mapping[label] for label in y_text])

# Convert DataFrames to numpy arrays
X_audio = X_audio.to_numpy()
X_resid = X_resid.to_numpy()
X_text  = X_text.to_numpy()

# Define models
log_reg = LogisticRegression(max_iter=1000, random_state=42)
random_forest = RandomForestClassifier(n_estimators=100, random_state=42)

# Evaluate models
results = []
results.append(
    evaluate_model(X_text, y_text, log_reg, "Logistic Regression (X_text)"))
results.append(
    evaluate_model(X_audio, y_audio, log_reg, "Logistic Regression (X_audio)"))
results.append(
    evaluate_model(X_resid, y_resid, log_reg, "Logistic Regression (X_resid)"))
results.append(
    evaluate_model(X_text, y_text, random_forest, "Random Forest (X_text)"))
results.append(
    evaluate_model(X_audio, y_audio, random_forest, "Random Forest (X_audio)"))
results.append(
    evaluate_model(X_resid, y_resid, random_forest, "Random Forest (X_resid)"))


# Convert results to DataFrame
results_df = pd.DataFrame(results).drop(columns=["Confusion Matrix"])

print("\nClassification Results:")
display(results_df)

# Plot classification results
plt.figure(figsize=(10, 5))
results_df.set_index("Model").plot(kind="bar", figsize=(10, 5))
plt.title("Classification Performance Comparison", fontsize=14)
plt.ylabel("Score", fontsize=12)
plt.xticks(rotation=45, fontsize=10)
plt.yticks(fontsize=10)
plt.legend(loc="lower right", fontsize=10)
plt.grid(True, linestyle="--", linewidth=0.5, alpha=0.7)
plt.show()

Classification Results:

	Model	Accuracy	F1 Score	AUC-ROC
0	Logistic Regression (X_text)	0.000000	0.000000	0.110647
1	Logistic Regression (X_audio)	0.979167	0.979601	0.996384
2	Logistic Regression (X_resid)	0.989583	0.989279	0.999267
3	Random Forest (X_text)	0.000000	0.000000	0.097023
4	Random Forest (X_audio)	0.895833	0.904243	0.989440
5	Random Forest (X_resid)	0.989583	0.989279	0.999817

This last experiment highlights an important distinction:

Short sentences provide fewer vocal cues, making it harder for models to distinguish tone from linguistic content. In raw audio embeddings, tone information is often entangled with what is being said, making classification more difficult. This is where residual embeddings provide a significant advantage, as they strip away content-related noise and preserve only vocal tone and style.
Long sentences, on the other hand, naturally contain more vocal evidence of tone, making classification easier across all models—even when using raw audio embeddings. With longer utterances, the additional speech data allows tone to emerge more distinctly, reducing the need for content filtering. Consequently, while residual embeddings still perform best, the gap between raw and residual embeddings becomes less pronounced.

In summary, residual embeddings are most beneficial when tone cues are sparse or ambiguous, such as in short speech segments. When ample vocal evidence is available (as in longer utterances), models can more easily capture tone directly from raw embeddings, making content removal less critical.

🔍 Key Takeaways

✅ Residual embeddings cluster clearly by tone, unlike raw audio embeddings, making them a more effective representation of vocal style.

✅ Removing linguistic content enhances classification accuracy, particularly in cases where tone is subtle or mixed with content.

✅ Even simple models perform well on residual embeddings, proving that the extracted paralinguistic features are well-structured and easy to classify.

✅ Residual embeddings are especially beneficial for short sentences, where tone cues are limited and harder to separate from content in raw audio embeddings.

✅ With longer sentences, tone information becomes more pronounced in raw audio embeddings, naturally improving classification performance across the board and reducing the gap between raw and residual embeddings.

✅ Residual embeddings provide the greatest advantage when tone evidence is sparse or ambiguous, reinforcing their usefulness in analyzing brief speech segments or detecting subtle vocal cues.

Discrepancy Index – A Future Research Direction

So far, we’ve demonstrated that residual embeddings are more effective at capturing tone and style, while text embeddings reflect semantic content. But what happens when textual sentiment and vocal tone don’t align?

This opens up an exciting research question: Could we quantify such discrepancies to detect sarcasm, persuasion, or even deception?

💡 The idea:

We propose a Discrepancy Index, a metric to measure mismatches between text sentiment (what is said) and vocal tone (how it is said).

🔹 Potential applications:

📢 Sarcasm detection → Positive words, but an angry tone? That’s likely sarcasm.
🕵️‍♂️ Deception & hesitation analysis → A positive tone but hesitant, uncertain speech? Possible deception or reluctance.
🎭 Politeness & persuasion → Negative wording, but spoken warmly? Maybe softening bad news.

🔬 Next steps for exploration:

Train a model to classify text sentiment (positive/negative).
Train another model to classify vocal tone from residual embeddings.
Compute a Discrepancy Score: When sentiment and tone contradict, is it meaningful?

We hypothesize that by detecting mismatches between vocal tone and textual sentiment, we can build AI models that detect sarcasm, deception, and persuasion more effectively.

For instance, in customer service, a polite “We’re happy to help” in a frustrated tone might signal dissatisfaction. In earnings calls, a neutral financial update with a hesitant tone might indicate hidden concerns.

These findings reinforce that AI can learn not just what we say, but how we say it. By exploring the Discrepancy Index for sarcasm, persuasion, and deception, we take another step toward teaching machines to ‘read the room’.

Disentangling Speech Embeddings: Removing Text Content from Audio Embeddings with Regression

Sun, 02 Feb 2025 00:00:00 +0000

Disentangling Speech Embeddings: Removing Text Content from Audio Embeddings with Regression

tl;dr: Speech embeddings encode both text content and audio-specific features (e.g., speaker identity, prosody). This blog explores how to disentangle these components by using Ridge regression to remove text content, leaving residual embeddings that focus on speaker-specific characteristics. We analyze these residual embeddings, and find that the residuals effectively isolate audio features.

Introduction
Data Preparation
Residualizing Audio Embeddings via Regression
Analyzing Embedding Similarities
Discussion and Conclusion

1. Introduction

Over the past few years, advances in deep learning—especially using transformer architectures and self-supervised learning—have revolutionized audio and speech processing. Modern models produce rich speech embeddings that encapsulate various aspects of the signal: background noise, speaker identity, prosody, and even the semantic content (i.e., what is being said).

However, if you plan to use these embeddings for downstream tasks, an important question arises: What exactly is contained in these embeddings? Are they dominated by the text content, or do they also capture pure audio characteristics? In this blog post, we walk through a notebook that tackles this question by “regressing out” the text content from the audio embeddings. We then analyze both the original and residual embeddings to see how the similarity patterns change, and whether we have isolated the speaker/audio-specific features.

Background Concepts

Before we dive into the analysis, let’s briefly define a few key concepts:

Embeddings: High-dimensional numerical representations of text or audio data, capturing semantic or speaker-specific information.
Cosine Similarity: A measure of similarity between two vectors, calculated as the cosine of the angle between them. It ranges from -1 (opposite) to 1 (identical).
Ridge Regression: A linear regression model with an L2 regularization term that prevents overfitting by penalizing large coefficients.

With these definitions in mind, let’s move on to the data preparation.

2. Data Preparation

We first load and preprocess our data. In our case, we have:

Text embeddings (shape 1536) stored in a Parquet file.
Audio embeddings (shape 768) stored as .npy files for each sentence and voice.

2.1 Loading Audio and Text Embeddings

We start by importing our libraries and reading in the embeddings.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import Ridge
from sklearn.metrics.pairwise import cosine_similarity

# Define a list of voice labels used in the recordings.
voices = [
    "alloy", "ash", "coral", "echo", "fable", "onyx", "nova", "sage", "shimmer",
]

# Load text embeddings and sentence metadata.
text_emb = pd.read_parquet("text_embeddings.parquet")
sentences = pd.read_parquet("earnings_calls_sentences.parquet")

# Process audio embeddings: compute a single (mean-pooled) vector per recording.
audio_embeddings = []
for idx in range(len(sentences)):
    for voice in voices:
        embedding = np.load(f"audio_embeddings/speech_{idx}/speech_{voice}.npy")
        # Mean pool over the time axis; adjust indexing as needed.
        single_embedding = embedding.mean(axis=1)[0].tolist()
        audio_embeddings.append([f"{idx}_{voice}"] + single_embedding)

2.2 Experimental Setup

Our experiments start with a dataset of 52 unique sentences, each spoken by 9 different voices (e.g., “alloy,” “ash,” “coral”). This results in a total of 468 audio recordings, with each sentence-voice pair represented by:

Text embeddings (dimensionality: 1536) encoding the semantic content of the sentence.
Audio embeddings (dimensionality: 768) capturing both speaker-specific characteristics and the spoken content.

This setup allows us to explore how embeddings encode information across voices and sentences and how residualizing affects these representations.

2.3 Building DataFrames

Next, we construct DataFrames for the audio embeddings. For each sentence (indexed by idx) and for each voice, we load the corresponding .npy file, compute the mean over time frames, and store the result. We then repeat the text embedding for each voice (since each sentence’s text is paired with multiple voices).

# Create a DataFrame with audio embeddings and set the index.
df_audio_emb = pd.DataFrame(audio_embeddings).set_index(0)
df_audio_emb.index.name = "sent_speaker"
display(df_audio_emb.head())

# Expand the text embeddings to match the number of audio samples.
exp_text_emb = []
for idx, row in text_emb.iterrows():
    for _ in range(len(voices)):
        exp_text_emb.append(row.values.tolist())
df_text_emb = pd.DataFrame(exp_text_emb)
display(df_text_emb.head())

	1	2	3	4	5	6	7	8	9	10	...	759	760	761	762	763	764	765	766	767	768
sent_speaker
0_alloy	-0.181595	0.044399	0.336770	0.053689	0.187663	-0.230498	0.022786	-0.104681	-0.193501	-0.176574	...	-0.056888	0.203571	0.096349	0.057072	-0.296644	-0.061813	-0.190370	0.177688	0.354035	-0.282887
0_ash	-0.136084	-0.002757	0.295544	0.086532	0.232084	-0.240367	0.033949	-0.110311	-0.150221	-0.166532	...	-0.029989	0.178773	0.108225	0.074687	-0.370774	-0.072837	-0.204104	0.226053	0.343412	-0.251210
0_coral	-0.158940	0.009858	0.358308	0.066840	0.209457	-0.220230	-0.010542	-0.112051	-0.242541	-0.176694	...	-0.013010	0.216197	0.078014	0.045757	-0.367586	-0.057285	-0.206471	0.237917	0.285875	-0.227131
0_echo	-0.147727	0.021007	0.367160	0.052192	0.219705	-0.233183	0.013074	-0.109856	-0.230388	-0.161419	...	-0.016526	0.195764	0.098858	0.127696	-0.308343	-0.074408	-0.170866	0.212565	0.339611	-0.250214
0_fable	-0.173835	0.020789	0.361774	0.049154	0.180092	-0.222793	-0.007481	-0.098986	-0.209910	-0.172046	...	-0.087433	0.161901	0.106060	0.113969	-0.340039	-0.061142	-0.185088	0.235554	0.332321	-0.299397

5 rows × 768 columns

	0	1	2	3	4	5	6	7	8	9	...	1526	1527	1528	1529	1530	1531	1532	1533	1534	1535
0	-0.017998	-0.02158	-0.007001	-0.015076	0.008172	-0.005641	-0.010977	-0.005072	-0.01783	-0.018438	...	0.004975	-0.005728	0.050969	0.001896	0.001191	-0.01849	-0.005182	-0.005217	-0.001707	-0.011934
1	-0.017998	-0.02158	-0.007001	-0.015076	0.008172	-0.005641	-0.010977	-0.005072	-0.01783	-0.018438	...	0.004975	-0.005728	0.050969	0.001896	0.001191	-0.01849	-0.005182	-0.005217	-0.001707	-0.011934
2	-0.017998	-0.02158	-0.007001	-0.015076	0.008172	-0.005641	-0.010977	-0.005072	-0.01783	-0.018438	...	0.004975	-0.005728	0.050969	0.001896	0.001191	-0.01849	-0.005182	-0.005217	-0.001707	-0.011934
3	-0.017998	-0.02158	-0.007001	-0.015076	0.008172	-0.005641	-0.010977	-0.005072	-0.01783	-0.018438	...	0.004975	-0.005728	0.050969	0.001896	0.001191	-0.01849	-0.005182	-0.005217	-0.001707	-0.011934
4	-0.017998	-0.02158	-0.007001	-0.015076	0.008172	-0.005641	-0.010977	-0.005072	-0.01783	-0.018438	...	0.004975	-0.005728	0.050969	0.001896	0.001191	-0.01849	-0.005182	-0.005217	-0.001707	-0.011934

5 rows × 1536 columns

Now we have two DataFrames:

df_text_emb of shape (n_samples, 1536), and
df_audio_emb of shape (n_samples, 768), where n_samples equals the number of sentences multiplied by the number of voices.

3. Residualizing Audio Embeddings via Regression

The key idea is to remove the text content from the audio embeddings. We assume that each audio embedding $ \mathbf{y} $ can be approximated as a linear function of the text embedding $ \mathbf{x} $:

$\mathbf{y} = W \mathbf{x} + \mathbf{e}$

where $W$ is the weight matrix and $ \mathbf{e} $ is the residual representing audio-specific information. We use Ridge regression to learn $W$.

3.1 Setting Up the Regression Problem

We convert our DataFrames into NumPy arrays and check their shapes.

# Convert DataFrames to NumPy arrays.
X = df_text_emb.values    # shape: (n_samples, 1536)
Y = df_audio_emb.values   # shape: (n_samples, 768)

print("Text embeddings shape:", X.shape)
print("Audio embeddings shape:", Y.shape)

Text embeddings shape: (468, 1536)
Audio embeddings shape: (468, 768)

3.2 Fitting a Ridge Regression Model

We now fit a Ridge regression model (with a chosen regularization strength) that maps text embeddings to audio embeddings.

# Use the entire dataset for training for simplicity.
X_train, Y_train = X, Y
X_test, Y_test = X, Y

# Fit the Ridge regression model.
alpha = 1.0  # Regularization strength; you may adjust this.
model = Ridge(alpha=alpha)
model.fit(X_train, Y_train)

Ridge()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

3.3 Computing Residuals

After fitting the model, we compute the predicted audio embeddings and then the residuals:

# Predict audio embeddings from text embeddings.
Y_pred = model.predict(X)

# Compute the residual: actual audio embedding minus predicted embedding.
E = Y - Y_pred
print("Shape of the residual embeddings:", E.shape)

# Optional: evaluate the model performance.
from sklearn.metrics import mean_squared_error
mse = mean_squared_error(Y_test, model.predict(X_test))
print("Test MSE:", mse)

# Create a DataFrame for the residual embeddings.
residual_emb = pd.DataFrame(E, index=df_audio_emb.index)
display(residual_emb.head())

Shape of the residual embeddings: (468, 768)
Test MSE: 0.0011371899030286064

	0	1	2	3	4	5	6	7	8	9	...	758	759	760	761	762	763	764	765	766	767
sent_speaker
0_alloy	-0.052219	0.008099	0.030596	-0.024522	-0.031399	-0.003683	0.003598	0.029022	0.005255	-0.011372	...	-0.027316	0.008942	0.010294	-0.032126	0.044079	-0.000491	-0.006691	-0.039610	0.013437	-0.027009
0_ash	-0.006709	-0.039057	-0.010630	0.008322	0.013022	-0.013552	0.014761	0.023392	0.048536	-0.001330	...	-0.000417	-0.015856	0.022170	-0.014510	-0.030051	-0.011516	-0.020425	0.008755	0.002814	0.004668
0_coral	-0.029564	-0.026442	0.052134	-0.011371	-0.009605	0.006586	-0.029729	0.021652	-0.043785	-0.011492	...	0.016562	0.021568	-0.008041	-0.043440	-0.026863	0.004037	-0.022792	0.020619	-0.054723	0.028747
0_echo	-0.018352	-0.015293	0.060987	-0.026018	0.000643	-0.006368	-0.006114	0.023847	-0.031632	0.003783	...	0.013046	0.001135	0.012803	0.038499	0.032380	-0.013086	0.012812	-0.004733	-0.000987	0.005664
0_fable	-0.044460	-0.015510	0.055601	-0.029056	-0.038970	0.004022	-0.026668	0.034717	-0.011154	-0.006844	...	-0.057861	-0.032728	0.020005	0.024772	0.000684	0.000180	-0.001409	0.018257	-0.008277	-0.043519

5 rows × 768 columns

4. Analyzing Embedding Similarities

To understand the impact of residualizing audio embeddings, we analyze how similarity patterns change before and after regressing out the text content. Specifically, we compute pairwise similarity matrices for both the original audio embeddings and the residual embeddings. We then compare these similarity matrices by examining their coefficient distributions, visualizing their structures, and summarizing them using average similarity scores across voices. This allows us to assess whether the residual embeddings effectively isolate speaker-specific features.

4.1 Pairwise Similarity with Cosine Similarity for Each Voice

We first compute pairwise cosine similarity among the residual embeddings and visualize the distribution of similarity values for each voice using histograms.

# Compute cosine similarity on the residual embeddings.
sim = cosine_similarity(residual_emb)
df_sim = pd.DataFrame(sim, index=residual_emb.index, columns=residual_emb.index)

# Plot histograms of the upper-triangle similarity values for each voice.
plt.figure(figsize=(12, 6))
alpha = 1.0  # starting alpha for histogram transparency

for voice in voices:
    # Select sample indices that contain the current voice.
    sample = [elem for elem in df_sim.index if voice in elem]
    mat = df_sim.loc[sample, sample]
    
    # Mask lower-triangle and diagonal values.
    mask = np.triu(np.ones(mat.shape, dtype=bool), k=1)
    upper_tri_values = np.where(mask, mat, np.nan).flatten()
    
    # Filter out NaN values.
    upper_tri_values = upper_tri_values[~np.isnan(upper_tri_values)]
    
    plt.hist(upper_tri_values, bins=100, label=voice, alpha=alpha)
    alpha -= 0.10

plt.legend()
plt.title("Histogram of Residual Embedding Similarities by Voice")
plt.xlabel("Cosine Similarity")
plt.ylabel("Frequency")
plt.show()

The histogram above displays the cosine similarity distributions of residual embeddings for each voice after regressing out the text content. Each colored distribution represents a specific voice (e.g., “alloy,” “ash,” “coral”), with the x-axis showing the cosine similarity values and the y-axis indicating the frequency of these values.

Key Observations:

Distinct Voice Distributions: The residual embeddings show noticeable differences in cosine similarity distributions for each voice, highlighting that voice-specific characteristics are preserved after text content is removed.

Cosine Similarity Range: Unlike the original audio embeddings, where cosine similarity values are tightly clustered due to shared text content, the residual embeddings exhibit a wider range of similarity values (from negative values to moderately high positives, up to ~0.8). This indicates reduced influence from shared text content.

To further illustrate the impact of residualization, we compare the similarity patterns of residual embeddings with those of the original audio embeddings. The next histogram visualizes the cosine similarity distributions of the original audio embeddings for each voice, offering a baseline to evaluate how much the shared text content influences these embeddings.

# Compute cosine similarity on the audio embeddings.
sim = cosine_similarity(df_audio_emb)
df_sim = pd.DataFrame(sim, index=df_audio_emb.index, columns=df_audio_emb.index)

# Plot histograms of the upper-triangle similarity values for each voice.
plt.figure(figsize=(12, 6))
alpha = 1.0  # starting alpha for histogram transparency

for voice in voices:
    # Select sample indices that contain the current voice.
    sample = [elem for elem in df_sim.index if voice in elem]
    mat = df_sim.loc[sample, sample]
    
    # Mask lower-triangle and diagonal values.
    mask = np.triu(np.ones(mat.shape, dtype=bool), k=1)
    upper_tri_values = np.where(mask, mat, np.nan).flatten()
    
    # Filter out NaN values.
    upper_tri_values = upper_tri_values[~np.isnan(upper_tri_values)]
    
    plt.hist(upper_tri_values, bins=100, label=voice, alpha=alpha)
    alpha -= 0.10

plt.legend()
plt.title("Histogram of Audio Embedding Similarities by Voice")
plt.xlabel("Cosine Similarity")
plt.ylabel("Frequency")
plt.show()

The histogram above displays the cosine similarity distributions for the original audio embeddings across different voices. Each colored distribution represents a specific voice (e.g., “alloy,” “ash,” “coral”), with the x-axis showing cosine similarity values and the y-axis indicating their frequency.

Key Observations:

Overlapping Voice Distributions: The similarity distributions for different voices overlap significantly, demonstrating that shared text content dominates the original audio embeddings.

High Similarity Concentration: The cosine similarity values for the original audio embeddings are tightly clustered in a high range, predominantly between 0.88 and 0.99, highlighting the strong influence of the text content.

Unlike the residual embeddings, the original audio embeddings exhibit a narrow spread of similarity values, indicating that speaker-specific features are less prominent in the original embeddings.

4.2 Same-Voice Similarity: Audio vs. Residual

In this section, we compare the cosine similarity patterns for recordings of the same voice (e.g., “alloy” vs. “alloy”) using both the original audio embeddings and the residual embeddings. The pairwise similarity matrices and histograms illustrate distinct differences between the two representations.

Original Audio Embeddings (Same Voice)

# Compute similarity for the same voice on audio embeddings.
voice_1 = "alloy"
voice_2 = "alloy"  # Same voice comparison.

sample_1 = [elem for elem in df_audio_emb.index if voice_1 in elem]
sample_2 = [elem for elem in df_audio_emb.index if voice_2 in elem]

pairwise_sim_same_audio = cosine_similarity(
    df_audio_emb.loc[sample_1],
    df_audio_emb.loc[sample_2]
)

plt.figure(figsize=(8, 6))
plt.pcolormesh(pairwise_sim_same_audio, cmap='viridis')
plt.title("Pairwise Similarity: 'alloy' vs. 'alloy' (Audio Embeddings)")
plt.colorbar()
plt.show()

plt.figure()
plt.hist(pairwise_sim_same_audio.flatten(), bins=100)
plt.title("Histogram: 'alloy' vs. 'alloy' (Audio Embeddings)")
plt.xlabel("Cosine Similarity")
plt.ylabel("Frequency")
plt.show()

print(pd.Series(pairwise_sim_same_audio.flatten()).describe())

The similarity matrix for the original audio embeddings shows consistently high values, with most cosine similarity scores tightly clustered around 0.98. This is reflected in the histogram, where the majority of values fall between 0.95 and 1.0.

count    2704.000000
mean        0.978689
std         0.013633
min         0.903744
25%         0.972316
50%         0.982532
75%         0.988346
max         1.000000
dtype: float64

Residual Embeddings (Same Voice)

# Compute similarity for the same voice on residual embeddings.
sample_1_resid = [elem for elem in residual_emb.index if voice_1 in elem]
sample_2_resid = [elem for elem in residual_emb.index if voice_2 in elem]

pairwise_sim_same_resid = cosine_similarity(
    residual_emb.loc[sample_1_resid],
    residual_emb.loc[sample_2_resid]
)

plt.figure(figsize=(8, 6))
plt.pcolormesh(pairwise_sim_same_resid, cmap='viridis')
plt.title("Pairwise Similarity: 'alloy' vs. 'alloy' (Residual Embeddings)")
plt.colorbar()
plt.show()

plt.figure()
plt.hist(pairwise_sim_same_resid.flatten(), bins=100)
plt.title("Histogram: 'alloy' vs. 'alloy' (Residual Embeddings)")
plt.xlabel("Cosine Similarity")
plt.ylabel("Frequency")
plt.show()

print(pd.Series(pairwise_sim_same_resid.flatten()).describe())

In contrast, the similarity matrix for the residual embeddings reveals a broader range of cosine similarity values, spanning from approximately -0.2 to 1.0. The histogram demonstrates more variability, with similarity values distributed more evenly across the range.

count    2704.000000
mean        0.460229
std         0.187644
min        -0.210560
25%         0.338727
50%         0.468087
75%         0.593228
max         1.000000
dtype: float64

4.3 Different-Voice Similarity: Audio vs. Residual

In this section, we analyze the similarity patterns for recordings of different voices (e.g., “alloy” vs. “coral”) using both the original audio embeddings and the residual embeddings.

The results provide insights into how shared text content and speaker-specific features influence embedding similarity.

Original Audio Embeddings (Different Voices)

# Compute similarity for different voices on audio embeddings.
voice_1 = "alloy"
voice_2 = "coral"  # different voice comparison.

sample_1 = [elem for elem in df_audio_emb.index if voice_1 in elem]
sample_2 = [elem for elem in df_audio_emb.index if voice_2 in elem]

pairwise_sim_same_audio = cosine_similarity(
    df_audio_emb.loc[sample_1],
    df_audio_emb.loc[sample_2]
)

plt.figure(figsize=(8, 6))
plt.pcolormesh(pairwise_sim_same_audio, cmap='viridis')
plt.title("Pairwise Similarity: 'alloy' vs. 'coral' (Audio Embeddings)")
plt.colorbar()
plt.show()

plt.figure()
plt.hist(pairwise_sim_same_audio.flatten(), bins=100)
plt.title("Histogram: 'alloy' vs. 'coral' (Audio Embeddings)")
plt.xlabel("Cosine Similarity")
plt.ylabel("Frequency")
plt.show()

print(pd.Series(pairwise_sim_same_audio.flatten()).describe())

The similarity matrix and histogram for the original audio embeddings show that even for different voices, cosine similarity values remain high, with most scores clustering between 0.96 and 0.98. This indicates that shared text content dominates the embeddings, overwhelming the differences between voices. The high similarity values reflect a lack of discrimination between different speakers when text content is present.

count    2704.000000
mean        0.968115
std         0.015492
min         0.885379
25%         0.962617
50%         0.972892
75%         0.978895
max         0.988612
dtype: float64

Residual Embeddings (Different Voices)

# Compute similarity for different voices on residual embeddings.
sample_1_resid = [elem for elem in residual_emb.index if voice_1 in elem]
sample_2_resid = [elem for elem in residual_emb.index if voice_2 in elem]

pairwise_sim_same_resid = cosine_similarity(
    residual_emb.loc[sample_1_resid],
    residual_emb.loc[sample_2_resid]
)

plt.figure(figsize=(8, 6))
plt.pcolormesh(pairwise_sim_same_resid, cmap='viridis')
plt.title("Pairwise Similarity: 'alloy' vs. 'coral' (Residual Embeddings)")
plt.colorbar()
plt.show()

plt.figure()
plt.hist(pairwise_sim_same_resid.flatten(), bins=100)
plt.title("Histogram: 'alloy' vs. 'coral' (Residual Embeddings)")
plt.xlabel("Cosine Similarity")
plt.ylabel("Frequency")
plt.show()

print(pd.Series(pairwise_sim_same_resid.flatten()).describe())

In stark contrast, the similarity matrix and histogram for the residual embeddings demonstrate a near-complete absence of similarity between different voices. The distribution of cosine similarity values spans a much wider range, centering around -0.15 with values as low as -0.5. This confirms that the residual embeddings have successfully removed the shared text content, leaving behind voice-specific characteristics. Since the embeddings now focus solely on speaker-related features, the similarity between different voices naturally drops.

count    2704.000000
mean       -0.148577
std         0.139143
min        -0.516812
25%        -0.244333
50%        -0.161543
75%        -0.065892
max         0.443491
dtype: float64

The last histogram is particularly compelling, as it illustrates the impact of residualization by showing the near-complete absence of similarity between embeddings from different voices.

4.4 Average Similarity Across Voices

Finally, in this section, we compute the average pairwise cosine similarity between all combinations of voices for both the original and residual embeddings. This provides a broader summary of the effects observed in Sections 4.2 and 4.3 by generalizing the analysis to all voice pairs. Instead of examining the entire distribution of similarities, we condense the information into average values for each voice combination.

def plot_heatmap(sim_matrix, title, xtick_labels):
    plt.figure(figsize=(8, 6))
    plt.pcolormesh(sim_matrix, cmap='viridis')
    plt.title(title)
    plt.colorbar()
    tick_positions = np.arange(sim_matrix.shape[0]) + 0.5
    plt.xticks(tick_positions, xtick_labels, rotation=90)
    plt.yticks(tick_positions, xtick_labels)
    plt.show()

def compute_avg_similarity(df_emb, voices):
    n = len(voices)
    avg_sim = np.zeros((n, n))
    for i, v1 in enumerate(voices):
        for j, v2 in enumerate(voices):
            sample_1 = [elem for elem in df_emb.index if v1 in elem]
            sample_2 = [elem for elem in df_emb.index if v2 in elem]
            sim_vals = cosine_similarity(df_emb.loc[sample_1], df_emb.loc[sample_2])
            avg_sim[i, j] = np.nanmean(sim_vals.flatten())
    return avg_sim

# Average similarity on audio embeddings.
sim_audio = compute_avg_similarity(df_audio_emb, voices)
plot_heatmap(sim_audio,
             "Average Similarity Between Voices (Audio Embeddings)",
             voices)

# Average similarity on residual embeddings.
sim_resid = compute_avg_similarity(residual_emb, voices)
plot_heatmap(sim_resid,
             "Average Similarity Between Voices (Residual Embeddings)",
             voices)

Original Audio Embeddings

The heatmap of average pairwise similarities for the original audio embeddings reveals uniformly high similarity values across all voice pairs, with most values exceeding 0.96. This aligns with the earlier observations that text content dominates the original embeddings. The lack of variability in the heatmap suggests that the original embeddings do not effectively distinguish between voices, as the textual content contributes significantly to the similarity scores.

Residual Embeddings

In contrast, the heatmap for the residual embeddings shows low or negative similarity values for different voice pairs, illustrating the success of residualization in removing text content. For the same voice combinations (diagonal elements of the heatmap), the values are noticeably higher, reflecting the preservation of speaker-specific features. The strong contrast between diagonal and off-diagonal values demonstrates that the residual embeddings capture meaningful differences between voices while retaining consistency within the same voice.

5. Discussion and Conclusion

Our analysis shows that the original audio embeddings exhibit high similarity across different voices, likely due to the dominating influence of text content (since all voices speak the same sentence). After applying Ridge regression to regress out the text component, the residual embeddings show:

High within-voice similarity: The residual still captures speaker-specific characteristics.
Low between-voice similarity: The shared text content is largely removed.

In this blog, we demonstrated how regression can effectively isolate speaker-specific features in audio embeddings by removing shared text content. However, we did not analyze in depth what characteristics remain in the residual embeddings. Do they capture pitch, tone, prosody, timbre, speaking rate, vocal intensity, or background noise? These are open questions worth further exploration. In future posts, we aim to explore these residual features in greater detail, shedding light on the nuances of what these embeddings truly represent and how they can be applied to more specialized audio analysis tasks.

Personal Reflections on NeurIPS 2024

Tue, 31 Dec 2024 00:00:00 +0000

Personal Reflections on NeurIPS 2024

Introduction

I attended NeurIPS 2024 in Vancouver with a few colleagues (Patrik, Kelvin, Lars, and Mathieu), despite the tempting weather and numerous events happening in Abu Dhabi during the winter season; Got a bad cold on the way back. This blog is an attempt to crystallize some of my personal takeaways from the conference, along with potential leads for further exploration. Naturally, it won’t be exhaustive, as I couldn’t attend every session and had other responsibilities to manage during the event. That said, I feel I gained a solid understanding of what had been happening in the fast-paced world of (academic) AI and ML over the past year.

NeurIPS (Neural Information Processing Systems), established in 1987, is one of the most prestigious conferences in artificial intelligence (AI) and machine learning (ML). Over the decades, it has grown into a global hub for researchers, practitioners, and industry leaders to share pioneering work, exchange ideas, and explore cutting-edge advancements. In 2024, NeurIPS reached a historic milestone with nearly 16,000 registrations, cementing its status as the largest academic conference in the field.

Beyond its peer-reviewed papers (talks, posters) and workshops, the event features a dynamic ecosystem of activities, including industry-sponsored events and an expansive sponsor expo (NeurIPS 2024 sponsors). This expo serves as a talent magnet, with companies—ranging from deep tech innovators to hedge funds and proprietary trading firms—eagerly recruiting PhD graduates and top researchers whose work is showcased at the conference. NeurIPS not only shapes the trajectory of AI research but also bridges academia and industry, fueling advancements across technology and finance.

Key Highlights from the Conference

I like to visualize papers on a map to navigate the main themes and find the ones I’m interested in, or even to grasp the broader trends of a conference. For example, I’ve done this for ICML 2024. However, this time, some others have already done a great job capturing the main topics of NeurIPS 2024, so no point duplicating their efforts. Here are two interactive websites that do it well:

A visualization of NeurIPS 2024 topics by Jay Alammar

NeurIPS 2024 Papers Map

Official NeurIPS visualization

NeurIPS 2024 Papers Map - Official

I somewhat prefer the ‘non-official’ version: I find it more convenient to navigate the papers by similarity.

Another interesting angle is the specialization by economic/geographic regions, which you can explore in this Sankey diagram.

Geographies-to-Topics Sankey

The conference is clearly dominated by China and the USA, which together account for about three-quarters of the papers. It’s unclear what methodology the Sankey uses—whether it attributes papers based on the authors’ university affiliations (likely) or tries to infer their nationalities from names (less likely). This is something I’ve explored before using LLMs to infer demographics from names—if you’re curious, I’ve written about it in this blog. Many papers from US universities are authored by Chinese researchers (or ABCs), highlighting China’s strong influence in AI research.

The specialization by region is also revealing. China stands out in Computer Vision and NLP and has a proportional strength in Graph Neural Networks. China’s dominance in Computer Vision suggests ongoing investments in this field, likely driven by applications in surveillance and autonomous systems. It’s notable that China is on par with the USA in RL & Robotics, but relatively weaker in “Explainable AI.” In contrast, the USA appears well-diversified across all sub-fields. Europe, while contributing fewer papers, shows a relatively stronger focus on “Explainable AI” and “Ethics & Safety.”

What’s missing? A good visualization illustrating the evolution of topics over time. I remember 10 years ago, it was all about supervised ML, structured predictions, dealing with missing and noisy labels, and active learning. Can we trace the transition from this focus on efficient labeling to the current zero-shot and few-shot paradigms? What caused some topics to fade away completely—were they trivialized by newer approaches? And do these newer paradigms fully subsume the older problems, or is it a case where they solve only certain aspects while the community shifts focus due to trends and “mode effects”?

Maybe I’ll take this up as a weekend project at some point—not super practical, but it would be fun to visualize how the field has evolved over the past 10–15 years just out of curiosity.

NeurIPS 2024 revealed some clear directions in AI/ML research. Multimodality continues to be a major focus, with researchers exploring how to integrate text, audio, video, and other modalities more effectively. Efficiency in LLMs is another prominent theme, whether through compression techniques, new architectures, or advances in inference. The role of reinforcement learning is also evolving—often intertwined with LLMs—to improve fine-tuning and enable more interactive applications. These trends highlight the ongoing balance between theoretical advancements and practical applications in the field.

Building on these broader trends, below are some of my personal takeaways and areas I found particularly interesting at NeurIPS 2024.

Personal Takeaways

Staying up-to-date in the field is a lot of work—it moves incredibly fast. It’s impossible to keep track of all the research, especially since a lot of it will turn out to be less relevant, follow wrong paths, or quickly get subsumed by superior models and approaches. Many ideas presented today will be discarded in the coming years as they are beaten by better solutions. Still, it’s a valuable exercise to try to understand and predict which pieces of work will be influential for the future of the field. I should probably commit to a fixed list for each conference—it’s too easy to ‘back trade’ and claim “I knew it!” after the fact. Feels a bit like stock picking, doesn’t it?

LLMs were, unsurprisingly, a major focus of the conference, often tying into more classic areas of ML. For instance, there were several papers exploring LLMs in combination with Reinforcement Learning (RL). If you look at the thematic map (below), you can see how LLMs subdivide into various sub-categories and connect with other branches of ML, such as privacy/security/adversarial attacks, multimodality, and alternative learning paradigms. One particularly interesting area is the investigation of using Transformers for state-space models, traditionally dominated by methods like Kalman filters.

NeurIPS 2024 Papers Map - Focus on LLMs

There were also lots of papers on applying LLMs to tackle mathematical and algorithmic problems, as well as in coding/programming tasks, like code generation and programming assistants. The industry is already seeing widespread adoption here—think Microsoft’s GitHub Copilot. The papers in this space seem to focus on pushing beyond the current capabilities or identifying and addressing corner cases that need more attention.

Another standout topic was LLM Compression Techniques, which focus on reducing the size, computational requirements, and memory footprint of large models while preserving as much of their performance as possible. The implications are obvious—lower costs, easier deployment in low-resource environments (like edge devices), and improved energy efficiency, which aligns with broader goals of sustainability. This area is crucial given how resource-hungry LLMs can be (GPUs/TPUs don’t come cheap, and energy costs are a concern for large-scale adoption). The emphasis on this topic shows the growing demand for solutions that make LLMs more accessible and environmentally sustainable.

Interestingly, the focus on efficient inference was reflected in the presence of several companies showcasing fast inference hardware, like Cerebras, D-Matrix, and SambaNova. Surprisingly, Groq wasn’t there—or at least I didn’t spot them.

NeurIPS isn’t typically the go-to conference for deep dives into knowledge graphs (KGs), but there were still some interesting contributions, particularly in how LLMs and KGs interact. For example, papers like KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge explore how LLMs can integrate with KGs, enriching their ability to reason over structured and unstructured data.

LLMs themselves can be thought of as fuzzy lookup tables or compressed knowledge bases, as they inherently store and compress vast amounts of the world knowledge they’re exposed to during training (cf. Hochreiter’s slide below). However, their “fuzziness” makes them less precise than KGs when it comes to exact reasoning, which is why the two technologies often complement each other.

LLMs are like databases - Sepp Hochreiter

Another promising direction is the use of LLMs to build, maintain, and enhance ontologies or KGs—tasks that have traditionally been labor-intensive and require substantial manual effort. LLMs can help automate ontology learning, as seen in End-to-End Ontology Learning with Large Language Models, or assist with challenges like entity alignment despite noisy data (Entity Alignment with Noisy Annotations from Large Language Models).

The synergy between KGs and LLMs is notable because it bridges the precision of symbolic representations with the adaptability and generalization of neural models. Whether fine-tuning LLMs on KGs, using LLMs to extract and structure knowledge, or leveraging KGs to ground LLMs for better factual accuracy, this interplay is likely to grow into a critical subfield of AI research.

Another relatively significant chunk of papers at the conference focused on the theme of transformers for state space models (SSMs). The potential applications are broad, spanning time-series forecasting, control systems (e.g., dynamic modeling for robots or autonomous vehicles), and signal processing. This stream of research aims to merge the strengths of traditional SSMs, which excel at handling latent states and noise in dynamical systems, with transformers, known for their ability to model long-range dependencies and complex, non-linear relationships.

The idea is intriguing—by blending these paradigms, researchers hope to create models that are both expressive and interpretable, leveraging the strengths of both approaches. While transformers have revolutionized sequence modeling, they face challenges like high resource demands and limited interpretability, which this hybrid approach could potentially address.

Transformers for state space models

Mamba: A New Competitor to Transformers?

One particularly interesting development in this space is the emergence of Mamba, a relatively recent architecture introduced on December 1, 2023. Despite its youth, Mamba already has more than two dozen papers accepted at NeurIPS 2024 leveraging the architecture, suggesting it’s gaining traction rapidly. Could Mamba-like models represent the next trend in sequence modeling? It’s too early to tell, but the pace of adoption at this year’s NeurIPS hints at strong interest.

Mamba is positioned as an alternative to transformers, specifically designed to improve efficiency and interpretability for tasks where sequence modeling intersects with dynamical systems. This architecture seeks to address some of the computational and scalability challenges of transformers while retaining their ability to handle complex dependencies.

While I’m not deeply familiar with this literature yet, it’s clear that Mamba and transformer-SSM hybrids are generating a lot of excitement. There seems to be real potential here, not just for theoretical advancements but for practical applications in areas like economic and financial time-series forecasting or autonomous systems. I should probably find the time to catch up on this literature, as it feels like a promising development. Will Mamba-like models carve out a niche alongside or even rival transformers?

I’ve bookmarked a handful of papers that caught my attention and seem relevant for understanding Mamba and its applications. I might read them later… These include:

LLM-based agents are an exciting and rapidly growing trend in research, with significant promise for industrial applications. These agents leverage the capabilities of large language models to perform complex, autonomous tasks across various domains. The flexibility and generalization capabilities of LLMs make them a strong candidate for automating workflows, enabling better decision-making, and even simulating human-like collaboration. Below are some key themes and papers on this topic that stood out:

LLMs as Urban Agents:

Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation: This paper explores LLM-based agents in the context of urban mobility, treating them as “virtual residents” to generate and simulate personal mobility patterns. These applications hold potential for urban planning, traffic management, and optimizing public transport systems. While similar use cases have been studied in agent-based modeling literature, they often rely on simpler, non-LLM-based agents. The novelty here lies in incorporating LLM-based agents into the modeling process, enabling more sophisticated and adaptable simulations.

Coding and Bug-Fixing Agents

SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents: Investigates the use of LLM-based agents for testing and validating software bug fixes, providing insights into how autonomous agents can improve software reliability.
MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue ReSolution: MAGIS introduces a team of four specialized agents: a Manager, Repository Custodian, Developer, and Quality Assurance Engineer, each tailored for different aspects of software evolution. This multi-agent setup demonstrates how LLMs can coordinate complex, collaborative workflows in software development.

Data Science and Workflow Automation

Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?: This paper provides a sobering look at the limitations of current multimodal agents, particularly in automating data science workflows. According to the authors, these agents still struggle with GUI interactions, highlighting the challenges of translating high-level language capabilities into precise, context-sensitive actions.
Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning: Focuses on optimizing data for instruction tuning through LLM-based agents, demonstrating their utility in automating large-scale data preprocessing and curation tasks.

Multi-Agent Environments

AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making: Explores the design of adaptive environments where multiple LLM-based agents interact with social structures to solve decision-making problems. The emphasis on social dynamics and collaboration adds a layer of complexity, mimicking real-world team interactions.

Financial Decision-Making

FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making: FinCon employs a hierarchical structure with Manager and Analyst agents collaborating through natural language interactions. The system generalizes across financial tasks, including single-stock trading and portfolio management, showcasing the potential of LLM-based agents in quantitative finance.

Manager and Analyst agents - Automating the pod model?

Before LLM-based agents took center stage, the main “agents” discussed at ML conferences were those built on Reinforcement Learning (RL). As can be seen on the conference papers map, these two sub-fields are increasingly intertwined. Many RL agents now incorporate LLMs either as a core component or as part of their input pipeline. LLMs provide these agents with enhanced natural language understanding, which allows them to process unstructured or complex instructions, interact with users more naturally, and even reason in ways that traditional RL agents struggle with.

Interestingly, this interaction works in both directions. RL is not just a consumer of LLM capabilities—it’s also a tool for improving or aligning LLMs. Techniques like Reinforcement Learning from Human Feedback (RLHF) have been key to fine-tuning LLMs for tasks that require alignment with human values or preferences, such as making conversational agents more polite or factual.

The multimodal large language model (LLM) space is a growing area of research, evident from a distinct cluster of papers in the NeurIPS papers map. These models sit at the intersection of traditional LLMs (text-based) and systems designed for text-to-image and text-to-video generation. Among the various approaches, researchers are focusing on both speed improvements and expanding multimodal capabilities, including audio, visual, and temporal understanding.

Some papers tackle audio-visual learning, addressing how to combine spoken language with visual cues for tasks like captioning, transcription, and video understanding. There is also interest in audio-only tasks, though these remain a niche area within NeurIPS, as ICASSP (International Conference on Acoustics, Speech, and Signal Processing) is the more dedicated venue for such research.

Video-based LLMs (VLMs) are emerging as a focal point. These models aim to integrate textual descriptions with video inputs to build temporal understanding. However, this remains a challenge due to the complexity of aligning text with dynamic, multi-frame video content. Temporal coherence—understanding events in sequence—is an area where significant progress is needed.

Generating videos remains one of the hardest tasks in multimodal modeling. Many systems struggle to respect common physics, such as gravity or object permanence, leading to outputs that break realism. Despite these flaws, improvements in video-language models (VLMs) signal progress, and this area remains an active focus of the AI/ML community.

Modeling text, audio, and video together remains a complex task due to the differences in data structure, scale, and temporal characteristics of these modalities. While progress is being made, truly unified multimodal models are still at an early stage. Advancements in architecture design, pretraining strategies, and computational efficiency will likely be needed to handle these challenges effectively.

Multimodal data remains an underutilized source of knowledge. Current LLMs are thought to have covered much of the publicly available text-based data, and future improvements in AI understanding may depend on incorporating other modalities:

Audio: Podcasts and expert discussions capture nuanced information and perspectives not commonly available in written form.
Video: Videos contain knowledge about the physical world, including motion, spatial relationships, and common-sense physics, which text-based models cannot fully grasp.

If models like VLMs improve their ability to integrate and analyze these modalities, they could lead to better internal representations and broader applications in areas like robotics, education, and healthcare.

While multimodal LLMs are growing, diffusion models remain a prominent area of research, particularly in image generation. These models dominate this domain due to their ability to generate high-quality, high-resolution images.

A notable trend is the rise of Diffusion Transformers, which combine diffusion processes with the attention-based capabilities of transformers. These models are proving to be highly effective for generating detailed, structured images. However, new challengers like Diffusion Mamba are emerging, potentially offering a more efficient alternative to the transformer-based architecture.

Despite the ongoing dominance of diffusion models in image generation, GPT-like autoregressive (AR) models are now surpassing diffusion transformers in certain settings. Notably, the paper Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction received the NeurIPS Best Paper Award for its groundbreaking work. This research highlights how AR models can achieve state-of-the-art results in image generation by leveraging scalability and precision in next-pixel or next-scale predictions.

It has been a while—probably about five years—since I last followed the developments in Graph Neural Networks (GNNs), and NeurIPS 2024 gave me a useful update on how the field has evolved. GNNs are no longer confined to traditional message-passing paradigms; instead, the field has diversified into new architectures, scalability methods, and integration with other models like LLMs.

As a quick refresher, GNNs aim to predict various properties or relationships in graphs, tackling tasks like node classification, link prediction, graph classification, graph regression, dynamic graph prediction, and even graph generation. Graphs—or networks, as they’re often called in social sciences—are everywhere in science and industry, with applications ranging from fraud detection and drug discovery to marketing and social network analysis.

One interesting direction is the development of “universal” GNNs—models designed to generalize across different types of graphs and tasks. This feels a bit like the “GPT moment” in NLP, where the goal is to create a general-purpose model. Papers like ProG: A Graph Prompt Learning Benchmark and RAGraph: A General Retrieval-Augmented Graph Learning Framework explore this idea. That said, the concept still seems far from realization. Graph data can vary a lot—from molecular graphs to social networks—so building a one-size-fits-all solution is a tough challenge.

To dig deeper, I bookmarked a few more papers that caught my attention during the conference. These papers span foundational tasks, interpretability, and novel methods in GNNs:

Foundational Tasks: Mixture of Link Predictors on Graphs
GNNs and LLMs: LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
Interpretability: The Intelligible and Effective Graph Neural Additive Networks

And, finally, a potentially useful trick for the practitioner: Probabilistic Graph Rewiring via Virtual Nodes.

Overall, there is about five years of research to catch up on. It will be interesting to see whether graphs or time-series models will achieve their “universal” moment first—if such a thing is even possible for either field.

GNNs and causality appear close in the NeurIPS 2024 papers map, as both fields rely heavily on graphs—directed acyclic graphs (DAGs) in the case of causality. GNNs focus on modeling complex relationships and interactions in networks, often with a prediction angle, whereas causality shifts the focus to uncovering and quantifying cause-and-effect relationships, leaning more towards explanation.

GNNs and Causal AI

Within the causality cluster, there’s a clear split: Causal Discovery and Representation is positioned closer to GNNs, while Causal Inference and Experimental Design sits further away. This reflects the focus of each area—causal discovery often involves graph-based methods to uncover relationships, while causal inference is more about estimating effects once a causal structure is known.

Causal Discovery vs. Causal Inference - NeuRIPS 2024 Tutorial

Causal discovery tries to answer the question, “What causes what?” It works on identifying relationships between variables, often using observational data, and aims to represent these as a DAG or similar graphical structure. Causal inference, on the other hand, answers, “How much does X affect Y?” It focuses on quantifying effects under interventions and testing the robustness of those conclusions. Together, they form a complementary loop: discovery generates hypotheses about causality, and inference tests and refines them.

While I don’t actively use causal discovery methods in my work, I’ve picked up some high-level insights from colleagues who specialize in this area. For domains where interventions are difficult or impossible, causal discovery becomes the primary focus. However, finding robust results in practice is incredibly challenging. It would be useful to see a systematic review ranking the applications of causal discovery methods from “easy” to “hard/not yet applicable”. I don’t have the bandwidth to experiment widely, but clarifying where the field stands in terms of practical applications would be helpful.

Causal discovery is an exciting area because of its potential to improve both interpretability and robustness in AI systems. Its applications span a wide range of fields, from healthcare to economics. A few papers and tools in this space caught my attention:

Causal-learn: Causal Discovery in Python: A Python library designed to make causal discovery more accessible.
Causal Dependence Plots: This approach visualizes how an outcome depends on changes in a predictor, along with the subsequent causal changes in other predictors.
Do causal predictors generalize better to new domains? Predictors using all available features, regardless of causality, have better in-domain and out-of-domain accuracy than predictors using causal features (!).
Discovery of the Hidden World with Large Language Models A paper showing how LLMs and causal discovery methods can complement each other in factor proposals.
Causal vs. Anticausal merging of predictors Authors show that if we observe all bivariate distributions, the CMAXENT solution reduces to a logistic regression in the causal direction and Linear Discriminant Analysis (LDA) in the anticausal direction.

Overall, while the theoretical tools in causal discovery are advancing, applying them effectively in real-world scenarios seems to remain a big challenge. It’s an area worth watching.

Moving from causality to another application-driven area, healthcare stood out as a distinct cluster on the NeurIPS 2024 map, positioned between ‘Large Language Models’ and ‘Multimodal Large Language Models,’ with 14 papers focused on LLM applications in medicine.

AI for Healthcare

Healthcare has long been a major user of applied causality, particularly through RCTs (randomized controlled trials) and causal inference methods. What’s new in these NeurIPS contributions is the focus on leveraging LLMs and introducing novel benchmarks. These papers primarily explore how LLMs can assist in medical tasks while starting to touch on some notion of causality—especially in understanding and reasoning about the decisions made by these models in high-stakes environments like medicine.

Interestingly, this highlights a potential convergence: while healthcare has historically used causality to ensure robust and interpretable outcomes, the next step may involve integrating causality into LLMs themselves. This is an active area of research, and it was even explored in a NeurIPS 2024 tutorial (picture below).

LLMs and Causality - NeuRIPS 2024 Tutorial

While I don’t work in this area (data science for healthcare), it’s of interest to ADIA Lab—an independent laboratory dedicated to data and computational sciences. Exploring these works could inspire research collaborations or yield practical insights.

What makes this collection particularly interesting is its focus on practical constraints and high-stakes decision-making, hallmarks of the medical field. Strict data privacy regulations, the need for interpretable models, and the severe consequences of errors set a high bar for performance. These challenges force researchers to design rigorous benchmarks and address real-world usability, offering valuable lessons for other domains where decisions carry material consequences.

Across these papers, several common themes emerge: improving the interpretability and reliability of LLMs, designing benchmarks tailored to medical tasks, and addressing safety, bias, and real-world usability. Here’s a brief overview of the contributions:

Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE A multi-task model integrating six medical tasks, from question answering to image classification.
PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications The first pediatric-specific LLM for Chinese medical applications.
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making A multi-agent framework for collaborative decision-making, excelling in multi-modal reasoning tasks.
MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning A system designed for question-asking to improve reliability in clinical reasoning.
Instruction Tuning Large Language Models to Understand Electronic Health Records Focuses on improving LLM performance in answering EHR-related patient queries.
DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models A dataset for evaluating diagnostic reasoning and interpretability compared to human doctors.
A teacher-teacher framework for clinical language representation learning A framework for aligning pre-existing models to better represent clinical notes.
MedJourney: Benchmark and Evaluation of Large Language Models over Patient Clinical Journey A benchmark covering four stages of a patient’s clinical journey, with tasks across 12 datasets.
MEDCALC-BENCH: Evaluating Large Language Models for Medical Calculations Focused on evaluating LLMs’ ability to perform medical calculations, highlighting their current limitations in clinical settings.
EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries A benchmark built on discharge summaries, evaluating LLM performance in answering patient-specific questions.
EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records A framework for verifying consistency in EHRs.
MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models A benchmark dataset measuring medical safety in LLMs, emphasizing fine-tuning for improved performance.
SM3-Text-to-Query: Synthetic Multi-Model Medical Text-to-Query Benchmark A benchmark of diverse natural language queries for medical tasks.
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias Highlights biases in LLMs’ disease prevalence representation across demographics.

These papers demonstrate the ongoing efforts to create safe, interpretable, and practical AI systems for healthcare. Even for those outside the domain, the rigor and high standards required for medical applications can serve as a model for developing AI in other critical areas.

Climate science is another area of focus for ADIA Lab, which has a natural interest in understanding long-term environmental trends and their impacts.

What’s the difference between climate and weather? In simple terms, weather describes the short-term atmospheric conditions in a specific place—think hours to days. Climate, on the other hand, is the average of weather conditions over a much longer period, typically decades or centuries. While weather forecasts tell you if it will rain tomorrow, climate models help predict long-term changes like global warming or shifts in precipitation patterns.

Climate papers:

Validating Climate Models with Spherical Convolutional Wasserstein Distance Introduces a method to measure differences between climate models and reanalysis data, ensuring better model validation.
ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction Proposes a benchmark to extend predictability from weeks to months, filling a crucial gap in climate forecasting.
Probabilistic Emulation of a Global Climate Model with Spherical DYffusion Explores whether the success of data-driven methods in weather forecasting can extend to the complexities of climate modeling.

Weather papers:

Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models Introduces LM-WEATHER, a model outperforming state-of-the-art results across tasks like forecasting and imputation.
Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling Combines physics for large-scale weather patterns with AI for adaptive corrections, achieving state-of-the-art performance at multiple timescales.
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks Combines latent-variable modeling with graph-based forecasting for more flexible and reliable probabilistic weather predictions.
Scaling transformer neural networks for skillful and reliable medium-range weather forecasting A transformer-based model delivering state-of-the-art performance, especially beyond 7-day forecasts, while using significantly less training data and compute.

These papers highlight the growing integration of physics-informed AI and advanced deep learning methods in tackling the challenges of both weather and climate prediction.

There were several other topics at NeurIPS 2024 that I didn’t dive into, such as molecular and quantum computing, autonomous driving, and low-level optimization techniques for ML models. While these areas are undoubtedly interesting and important, they are a bit far from my current interests and expertise.

What’s next?

Looking back at NeurIPS 2024, there are plenty of ideas to dig into and explore further. Here’s what I plan to focus on next:

Reach out to some of the authors of papers that caught my attention and invite them to present their work at the Abu Dhabi Machine Learning Meetups (or the Hong Kong ones for HK-based researchers). These sessions are a great way to learn directly from the researchers and get a better understanding of their work in a more pedagogical setting.
Try out a couple of GitHub repos associated with papers I bookmarked. If anything interesting comes out of playing with the implementations—whether insights or practical tweaks—I’ll blog about it. Not necessarily aiming for something groundbreaking, but more as a way to deepen my understanding.
Work through the stack of papers I’ve bookmarked from the conference. I’m particularly interested in topics like multimodal LLMs, Diffusion Transformers, and the Mamba architecture—these seem like promising directions worth exploring further.
Start keeping tabs on the emerging themes and discussions as they develop toward ICML 2025. It’s always better to go into a conference with a clear idea of what to prioritize.
Spend a bit more time understanding areas I’m less familiar with, like video-language models or the integration of text, audio, and video. These seem like foundational challenges, and it’s probably the next big frontier in AI.

Plenty to keep busy with. These conferences always leave me with a mix of curiosity and a bit of FOMO—there’s so much happening that you can’t possibly cover it all…

For those in Abu Dhabi (or Hong Kong), consider joining one of our upcoming Machine Learning Meetups. We’ll be discussing some of these trends in more depth, and it’s a great chance to learn from each other.

Wrapping up

NeurIPS 2024 brought a wide range of progress in AI/ML, from foundational work on multimodal models to practical applications in agents and generative systems. As always, it’s impossible to cover everything, but the conference gave me a good sense of where the field is heading and plenty of ideas to dig into. It’ll be interesting to see how these trends develop over the next year.

Are there any under-the-radar papers or trends I missed? Let me know!

What’s in a Name? AI Meets the Sociology of Naming

Sun, 17 Nov 2024 00:00:00 +0000

What’s in a Name? AI Meets the Sociology of Naming

Introduction: Context of this blog

My first encounter with this topic occurred while working on clustering financial time series to identify relative value opportunities in credit default swaps and develop alternative risk models. Around the same time, I was exploring open data sources and came across yearly datasets released by INSEE, which detailed the number of first names given to people born each year in France. These datasets allowed for the reconstruction of time series over the past century, enabling analyses such as measuring correlations between first names, clustering based on attribution patterns, and identifying similarities and trends.

This became an excellent use case for testing the flexibility of the DataGrapple platform, a tool designed to visualize CDS spread time series and hierarchical clustering from my research. Collaborating with Philippe Donnat and his team at the time, we integrated the first-name time series and hierarchical clusters into the platform, showcasing its visualization capabilities. Alongside this technical work, I developed an interest in the sociological aspects of naming and purchased Baptiste Coulmont’s book, which delves into the sociology of first names in France.

Fast forward 10 years, I encountered a tangentially related problem while working with my colleagues Abdulla and Khaled, who are pursuing PhDs in Data Science. As part of their research, they explored a dataset containing names (of both individuals and companies) but with few other accompanying features. Recognizing that names often encapsulate rich information about the entity they represent (see the field of onomastics), we explored the idea of using Large Language Models (LLMs) to extract characteristics from names and enrich the original dataset.

Although the research is still in its early stages, Abdulla and Khaled have already summarized some of their findings in two preprints:

Interestingly, these same tools have the potential to advance historical research, particularly in fields like prosopography, where patterns across groups of individuals—such as their names, origins, and societal roles—reveal broader historical dynamics. To better appreciate the possibilities for combining AI with such research, it’s worth delving into the historical development of socio-onomastics, a discipline that has long sought to understand the cultural and social significance of names.

Introduction: Brief history of socio-onomastics

The sociology of first names and surnames is not a new subject. Studies on the topic date back as early as 1681, such as the Traité de l’origine des noms et des surnoms (apparently available on Amazon).

This field, known as socio-onomastics, emerged as a sub-discipline of sociology in the late 1970s.

In France, sociologist Baptiste Coulmont has studied this topic for over two decades. He authored Sociologie des prénoms (2011, with a third edition published in 2022) and hosts a website illustrating the relationship between first names and results on the French national exam.

In France, a person’s full name (first name + last name) can reveal a surprising amount of information, such as gender, year of birth, social status, and even geographic origin—often pinpointing the region and sometimes even the specific village. This phenomenon is particularly pronounced in France, which boasts more than 900,000 different surnames). However, similar patterns can be observed to varying degrees in other parts of the world.

Key takeaways from socio-onomastics research

Cultural Influence: Media, religion, and globalization shape naming trends.
- “Kevin” in France became popular in the 1990s due to American pop culture, including actors like Kevin Costner.
- “Chloé” gained traction in part due to its association with the French luxury fashion brand.
- “Marie” remained dominant in earlier centuries because of its connection to the Virgin Mary and Catholic traditions.
Social Stratification: Certain names are linked to class or community perceptions.
- “Éléonore” or “Hugo” are often associated with upper-class or more academically inclined families in France.
- “Jordan” or “Dylan” are sometimes linked to working-class families, reflecting aspirational naming trends inspired by celebrities or sports figures.
- “Kevin” became stigmatized over time in France, being stereotypically linked to lower social classes.
Regional Identity: Names can pinpoint geographic and cultural origins.
- “Jean-Baptiste” is more common in rural, Catholic regions of France, reflecting traditional values.
- In Brittany, names like “Gwenaël” or “Maël” are tied to Celtic heritage and local identity.
- Surnames like “Le Goff”: This name is prevalent in Brittany, a region in western France, and reflects the area’s Celtic linguistic heritage.
- Surnames like “Barthez”: Common in Occitan-speaking regions such as southern France, particularly in Languedoc.
- Surnames like “Noël”: Frequently found in northeastern France, particularly in Lorraine, where it often reflects historical Catholic traditions associated with Christmas.
- Surnames like “Dubois”: While common across France, it historically originates from regions with significant woodland areas, such as Normandy or Picardy, as it means “of the woods.”
- Surnames like “Schmitt”: This surname is common in Alsace and reflects the region’s Germanic influence, as Alsace has historically shifted between French and German control. “Schmitt” is derived from the German word Schmied, meaning “smith.”
Generational Shifts: Popularity cycles reflect evolving societal values.
- “Jacques,” once a staple name for boys in older generations, has largely fallen out of favor in modern France.
- “Emma” surged in the 2000s as a modern, elegant alternative to older names like “Marie.”
- Names like “Théo” and “Léo” are increasingly popular among younger parents, reflecting contemporary naming preferences.
Ethnic Identity: Names often preserve cultural roots within diverse societies.
- “Mohamed” remains a popular choice among families of North African origin in France, reflecting cultural and religious ties.
- “Giuseppe” or “Enzo” among Italian immigrant communities highlights the retention of cultural heritage.
- “Wei” or “Xiao” are common names among Chinese immigrants, signaling strong ties to their linguistic and cultural origins.

Case Study: Gautier

What does ChatGPT 4o know about “Gautier”?

Prompt:

What can you tell about the first name “Gautier” in this context?

ChatGPT 4o:

The first name Gautier offers an interesting lens for socio-onomastics in the French context, as it reflects cultural, historical, and social influences:

Historical and Cultural Context

Origins: The name Gautier is derived from the Germanic Waldhari (wald- meaning “rule” and -hari meaning “army”). It has deep medieval roots and is closely tied to the Frankish heritage in France, making it a historically noble or prestigious name.
Popularity: Gautier has experienced revivals during different periods. In the 19th and early 20th centuries, it was relatively rare, often associated with historical or literary connotations, such as the poet Théophile Gautier. Its popularity increased in the late 20th century as part of a broader trend of reviving older, traditional French names.

Social Class: In modern times, Gautier is often associated with upper-middle-class families or parents seeking a name that blends tradition and sophistication.
Spelling Variants: The choice of spelling can also reveal social nuances:
- Gautier (traditional and French spelling) tends to appear among families with an appreciation for history or classic French culture.
- Gauthier (with an “h”) is equally traditional but sometimes perceived as slightly more formal or tied to surnames that have become first names.

Regional Relevance

Geographical Association: The name may be more common in regions of France with strong medieval legacies or historical ties to Frankish culture, such as the Loire Valley or Burgundy. However, it is not strongly tied to any one region, making it relatively “neutral” geographically.

Abstract Pattern

Generational Shift: Like many older French names, Gautier has benefited from a revival trend among parents looking for names that are both distinctive and rooted in French tradition. This reflects broader societal interest in blending heritage with modern identity.

In summary, Gautier serves as a good example of a name that is culturally rich, socially nuanced, and representative of broader trends in French naming practices.

Prompt:

If you were to provide an educated guess, what is the most likely year of birth of “Gautier”?

ChatGPT 4o:

Based on naming trends in France, an educated guess for the most likely year of birth for someone named Gautier would fall in the 1990s to early 2000s. Here’s why:

Revival of Traditional Names: During the late 20th century, there was a resurgence of interest in older, classic French names, including medieval and aristocratic names like Gautier. This aligns with a broader cultural trend toward reviving names with historical significance.
Distinctive yet Timeless Appeal: Names like Gautier became attractive to parents who wanted something traditional yet distinctive, without being overly old-fashioned.
Peak Popularity Data: While exact statistics for Gautier may not always be available, names in the same category (like Arthur or Thibault) typically saw a rise in the 1990s and early 2000s.

If we were to pick a single most likely year, 1995 would be a reasonable estimate, as this was a peak period for names of this type in France. However, it would still be commonly used throughout the decade surrounding this time.

Note: Data sourced from INSEE, showing the annual number of individuals named ‘Gautier’ born in France:

Prompt:

If you were to provide an educated guess, what is the most likely average grade for “Gautier” at the French Baccalauréat (French national exam)?

ChatGPT 4o:

Analyzing the performance of students named “Gautier” in the French Baccalauréat reveals notable trends:

High Success Rate: In 2023, 100% of candidates named “Gautier” passed the Baccalauréat.
Mentions Très Bien: The name “Gautier” is among those whose bearers frequently achieve the highest honors, indicating strong academic performance.

These patterns suggest that students named “Gautier” often excel in their studies, achieving commendable results in the Baccalauréat.

Note: Data sourced from Baptiste Coulmont’s website, illustrating performance of ‘Gautier’ vs. the national average results in the French Baccalauréat:

Let’s now combine the first name “Gautier” with the surname “Marti” to analyze the sociological insights a full name can reveal.

Prompt:

Based solely on the combination of the first name “Gautier” and the surname “Marti,” and without referencing any specific individual, what can be inferred about the person’s likely cultural background, regional origin, and other sociological aspects?

ChatGPT 4o:

Analysis:

The name “Gautier Marti” offers insights into cultural and regional origins:

First Name: Gautier
- Origin: “Gautier” is the French form of “Walter,” derived from the Germanic elements “wald” (rule) and “hari” (army), meaning “ruler of the army.”
- Usage: Predominantly used in France, “Gautier” is a masculine name with historical significance, often associated with medieval French nobility.
Surname: Marti
- Origin: “Marti” is a surname found in various cultures:
  - Catalan: Derived from the personal name “Martí,” a form of “Martin,” meaning “of Mars” or “warlike.”
  - Swiss German and South German: A diminutive of “Martin.”
  - Italian: Possibly from the medieval Greek “Martios,” meaning “March,” indicating a connection to the month.
- Usage: Common in Catalonia (Spain), parts of France, Switzerland, and Italy.

Note: Interestingly, I briefly considered the army as a potential career path, and went through the competitive exams for officer positions in the French Air Force and the French Navy.

Sociological Inferences:

Cultural Background: The combination suggests a French cultural background, given the French first name “Gautier” and the presence of the surname “Marti” in southern France.
Regional Origin: The surname “Marti” is prevalent in southern France, particularly in regions like Languedoc and Provence.
Historical Context: The name “Gautier” has medieval French associations, while “Marti” may indicate Catalan or southern French heritage.

“Gautier Marti” likely denotes a male individual of French nationality, possibly from southern France, with a name reflecting both medieval French and regional influences.

Overall Hypothesis:

Gautier Marti is likely a male individual of French nationality, born in the 1990s, with a mixed northern and southern French heritage. The name suggests an upper-middle-class background and possibly a family with a strong sense of cultural or historical appreciation.

Note: ChatGPT 4o is accurate in its assessment of me, based on first name + last name only!

Modern Tools and AI in Onomastics

As demonstrated in the anecdotal “Gautier” case study, Large Language Models (LLMs) can infer a surprising amount of background information about a person based solely on their first name—and even more accurately when a last name is included. These models, trained on vast datasets, excel at identifying cultural, historical, and regional naming patterns, providing insights that were once the exclusive domain of sociological expertise.

Together with Khaled and Mathieu, we aimed to provide a more comprehensive evaluation of LLMs’ capabilities, extending beyond the relatively “easy” case of French nationals. Our focus was on assessing performance across datasets with different geographic emphases: an American-focused dataset (Florida Voters Registration), a global dataset (Wikipedia People, albeit with a strong American bias), and an Asia-focused dataset (the SFC register of Hong Kong finance professionals, with a strong emphasis on Chinese names).

The results are detailed in Khaled’s paper, Enriching Datasets with Demographics through Large Language Models: What’s in a Name?. In summary, we found that these models are highly accurate in predicting gender, ethnicity, and nationality but face greater challenges when estimating age or date of birth. Some countries lack clear trends in naming patterns over time. For instance, in countries where first names are tied to religion, names tend to be timeless and remain in consistent use across generations. In others, like China, unisex names present difficulties in determining gender. Additionally, some Asian countries have a very limited pool of surnames—for example, South Korea, where Kim accounts for 21.5% of the population, or Vietnam, where Nguyễn is used by 39% of the population, reflecting historical practices of adopting the family names of prominent rulers.

Some results of the empirical study:

Conclusion: Names as windows into society

This brief exploration of socio-onomastics, combined with modern tools like Large Language Models (LLMs), demonstrates how much information can be inferred from something as seemingly simple as a name. By leveraging contemporary AI tools, we can uncover patterns tied to cultural, historical, and demographic factors.

While the results are promising, this work also underscores the inherent challenges and limitations of using AI for such tasks. Difficulties in handling unisex names, the timeless nature of religious naming traditions, and the prevalence of shared surnames in certain cultures highlight the complexity of this field. These challenges serve as a reminder that names are deeply intertwined with societal and historical contexts, which cannot always be fully captured by even the most advanced models.

Nonetheless, the integration of LLMs into this area of research presents exciting opportunities. In contexts where breadth of analysis is prioritized over depth, LLMs provide an effective means to scale investigations across vast corpora of text. Their ability to process large datasets efficiently enables broad analyses that complement traditional approaches. This synergy could open new avenues for insights in fields such as historical research through prosopography and demographic enrichment.

Ultimately, this blog represents a small step in understanding the role of names in society. While the findings suggest that names continue to offer valuable perspectives on identity and culture, we hope this work inspires further exploration at the intersection of sociology and AI research. Interestingly, this direction was not our original focus; we initially stumbled upon this topic while seeking to enrich a dataset with additional features to solve a practical prediction task.

[paper] Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

Sat, 02 Nov 2024 00:00:00 +0000

[paper] Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

GitHub repo of the paper.

Summary of the paper

The paper introduces STORM, a framework designed to help large language models (LLMs) create Wikipedia-like articles from scratch by focusing on a structured pre-writing phase. STORM’s core method involves synthesizing topic outlines through a unique process of perspective-guided question asking and multi-turn simulated conversations. These steps allow the LLM to gather information from diverse viewpoints, leading to comprehensive and organized outlines. Using these outlines, STORM generates well-structured articles with improved depth, coherence, and factual accuracy. The framework outperforms traditional retrieval-augmented approaches, though challenges like source bias and verifiability remain areas for future refinement.

Q&A with ChatGPT against the paper

Q: What are they key elements in the paper’s method to generate a high quality article?

A: The key elements of the method in the paper for generating high-quality Wikipedia-like articles involve the STORM framework, which structures the pre-writing process into several steps:

Perspective-Guided Question Asking: STORM starts by identifying multiple perspectives on a topic, helping generate diverse and in-depth questions that guide research effectively.
Simulated Conversations: By simulating a conversation between a topic expert and a Wikipedia writer, the model iteratively refines its understanding of the topic, asking follow-up questions to gather comprehensive information.
Outline Creation: The gathered information is organized into an outline using the model’s internal knowledge and curated sources. This outline serves as a structured foundation for the article.
Section-by-Section Writing: Each section of the article is generated based on the outline and relevant sources, ensuring coherence, coverage, and citation accuracy.

Q: How does STORM identify the multiple perspectives on a given topic?

A: STORM identifies multiple perspectives on a given topic by first surveying related Wikipedia articles. It retrieves the tables of contents from these articles to help pinpoint diverse viewpoints relevant to the topic. These perspectives, such as those of experts or laypeople, are then used to guide the generation of varied and specific questions. This approach ensures that the model gathers a broad, nuanced understanding of the topic, allowing it to explore and integrate different facets into the final article outline.

Q: What kind of framework is used in order to simulate the conversations?

A: STORM uses the DSPy framework to simulate conversations between a “Wikipedia writer” and a “topic expert.” This framework enables multi-turn, information-seeking dialogues, allowing the model to iteratively pose questions and gather responses. Each question-answer cycle builds on previous interactions, enhancing the depth and relevance of the information collected. Trusted online sources are incorporated to ground responses in verified data, supporting a reliable foundation for the final article outline and content generation.

Q: What are the weaknesses of this paper?

A: Some weaknesses in the paper include:

Bias and Verifiability Issues: STORM may inadvertently transfer biases from its information sources, leading to articles that sometimes lack neutrality or include unverifiable connections between facts.
Citation Limitations: Though citations are used, some generated connections between information pieces are not directly verifiable, raising accuracy concerns.
Simplified Task Scope: The framework primarily generates text-based articles, overlooking multimedia or structured data (like tables) typical of high-quality Wikipedia entries, which limits its realism for broader applications.

Q: Besides the Wikipedia use case, what could be other interesting and relevant tasks that this STORM framework could tackle?

A: Beyond Wikipedia, the STORM framework could be applied to other tasks requiring comprehensive research, structure, and neutrality, such as:

Educational Content Creation: Developing curricula, study guides, or academic summaries where balanced perspectives are essential.
Technical Documentation: Producing structured and informative guides, FAQs, or manuals for complex systems in software or engineering.
Market Analysis Reports: Compiling structured business or financial reports with varied perspectives (e.g., market trends, consumer insights, regulatory impacts).
Policy Briefs: Crafting policy documents that balance diverse viewpoints, ensuring well-rounded insights for government or NGO reports.

These tasks would benefit from STORM’s capacity to integrate varied information sources, generate outlines, and maintain organized content.

Q: What kind of questions would you suggest to ask against this paper to learn as much as possible from this paper?

A: Here are some in-depth questions to guide your learning from the paper:

Framework Design: What motivated the choice of using the DSPy framework for conversation simulation, and how does it enhance the pre-writing stage? Evaluation Metrics: How do the paper’s automatic and human evaluation metrics assess outline quality? What are their limitations? Perspective Discovery: What methods did the authors explore for identifying diverse perspectives, and could alternative approaches improve this process? Bias Handling: How does the framework address potential biases in information retrieval, and what solutions are suggested for future work? Applications Beyond Wikipedia: What specific adaptations might be necessary to apply STORM in fields like policy analysis or technical documentation?

Q: What kind of experiments do the author perform with their ablation study?

A: In their ablation study, the authors evaluate the impact of different components within the STORM framework on the quality of generated Wikipedia-like articles. They systematically remove or modify specific elements to assess their contributions:

Perspective-Guided Question Asking: They examine the effect of generating questions from multiple perspectives versus a single perspective, analyzing how this influences the depth and breadth of the collected information.
Simulated Conversations: The study assesses the role of multi-turn conversations between simulated roles (e.g., “Wikipedia writer” and “topic expert”) in enhancing the relevance and coherence of the gathered content.
Outline Generation: They investigate the importance of structured outline creation prior to writing, comparing outcomes with and without this step to determine its impact on the organization and coverage of the final article.

Q: What’s the conclusion of this ablation study?

A: The study concludes that each component of the STORM framework plays a vital role in producing high-quality, well-structured Wikipedia-like articles. Removing any of these elements results in a noticeable decline in the overall quality of the generated content.

While exploring frameworks implementing similar ideas, I came across GPT Researcher and decided to give it a try. It’s straightforward to install locally, and you can start experimenting right away using the default configuration—assuming you have API keys for OpenAI and Tavily.

Setting up the experiment is as easy as this:

Wait for 5 minutes while the Agent is doing its thing…

The results were somewhat disappointing (using only the web; not grounded in a relevant corpus). Not too surprising given the sources the Agent selected…

I’ll let you judge for yourself:

Agent Output:

Building an Equity Market Neutral Strategy Using Alternative Data

In the evolving landscape of quantitative finance, equity market neutral (EMN) strategies have emerged as a robust approach to achieving returns independent of market direction. These strategies, primarily employed by hedge funds, involve taking offsetting long and short positions in closely related stocks to hedge against market exposure. This approach seeks to capitalize on price discrepancies while maintaining a balanced portfolio that is theoretically immune to market volatility. The advent of alternative data has further revolutionized this domain, offering new avenues for enhancing the precision and effectiveness of EMN strategies.

Equity market neutral strategies are designed to exploit differences in stock prices by maintaining an equal amount of long and short positions in stocks that share similar characteristics, such as market capitalization or sector affiliation. This strategy is particularly appealing to institutional investors seeking to outperform traditional bonds without the high risk associated with more aggressive funds. For a comprehensive understanding of EMN strategies, Investopedia provides a detailed overview of their mechanics and benefits.

The integration of alternative data into EMN strategies represents a significant advancement in portfolio management. Alternative data, which includes non-traditional sources such as social media sentiment, satellite imagery, and transaction data, offers a granular view of market dynamics that traditional data sources might overlook. This data can enhance the predictive accuracy of stock price movements, thereby refining the selection of long and short positions within an EMN framework. Studies, such as those published in MDPI, have demonstrated the efficacy of using machine learning models like LSTM for stock prediction, leveraging alternative data to construct market-neutral portfolios.

Moreover, the systematic application of alternative data in EMN strategies can help mitigate risks and enhance returns. By employing sophisticated algorithms and machine learning techniques, portfolio managers can dynamically adjust their positions to maintain market neutrality, even as market conditions fluctuate. This approach is supported by insights from BlackRock, which emphasizes the importance of a systematic process in capturing alpha opportunities and managing risk exposures.

In conclusion, building an equity market neutral strategy using alternative data involves a nuanced understanding of both traditional financial principles and cutting-edge data analytics. As the financial markets continue to evolve, the integration of alternative data into EMN strategies will likely become an essential component for investors seeking to achieve consistent returns in an increasingly complex market environment.

Understanding Equity Market Neutral Strategy

Equity Market Neutral (EMN) strategies are designed to exploit pricing inefficiencies while maintaining a neutral exposure to the overall market. This report explores how to build an EMN strategy using alternative data, focusing on the integration of machine learning models, data collection techniques, and risk management practices.

Leveraging Alternative Data in EMN Strategies

Alternative data refers to non-traditional data sources that can provide unique insights into market trends. These include social media sentiment, satellite imagery, and web traffic data. The use of alternative data in EMN strategies can enhance the predictive power of models and uncover alpha opportunities that traditional data might miss.

Data Collection and Processing

The first step in incorporating alternative data into an EMN strategy is data collection and processing. Hedge funds and investment firms gather a wide array of data, including financial statements, earnings reports, and alternative unstructured data like news articles and social media sentiment (Aurum). The data is then cleaned and structured to be fed into quantitative models.

Signal Generation

Once the data is processed, the next step is generating trading signals. Quantitative models analyze the data to identify patterns and predict future stock movements. For instance, a model might use natural language processing (NLP) to gauge sentiment from news articles, which can then be used to predict stock price movements (Aurum). This approach allows for the creation of signals based on value, growth, price momentum, and quality.

Machine Learning Models in EMN Strategies

Machine learning models, particularly Long Short-Term Memory (LSTM) networks, have gained popularity in EMN strategies due to their ability to handle time series data effectively. These models can predict stock prices and returns with high accuracy, which is crucial for constructing a market-neutral portfolio (MDPI).

LSTM-Based Stock Prediction

LSTM networks are particularly suited for stock prediction due to their ability to capture long-term dependencies in data. By training on historical stock prices and alternative data, LSTM models can forecast future price movements, aiding in the selection of stocks for long and short positions in an EMN strategy.

Portfolio Construction

The construction of a market-neutral portfolio involves balancing long and short positions to achieve a beta close to zero. This is typically done by selecting an equal number of stocks for both positions, ensuring that gains from one side offset losses from the other (MDPI). The use of LSTM models in this process can enhance the robustness of the portfolio by improving stock selection accuracy.

Risk Management in EMN Strategies

Risk management is a critical component of EMN strategies, as it ensures that the portfolio remains market-neutral and minimizes exposure to systematic risk. This involves continuous monitoring and rebalancing of positions to maintain the desired risk profile.

Hedging Techniques

One of the main advantages of EMN strategies is their ability to hedge against market risk. By taking long positions in stocks expected to outperform and short positions in those expected to underperform, the strategy can mitigate the impact of market volatility (Picton Mahoney).

Diversification Benefits

EMN strategies offer diversification benefits by providing returns that are independent of market direction. This can be particularly valuable in periods of high market volatility, where traditional portfolios may suffer significant losses. By incorporating alternative data and machine learning models, EMN strategies can further enhance diversification by uncovering unique alpha opportunities (BlackRock).

Performance Evaluation and Adjustment

Evaluating the performance of an EMN strategy involves analyzing its risk-adjusted returns and ensuring that it meets the desired investment objectives. This requires regular backtesting and adjustment of models and data inputs to adapt to changing market conditions.

Backtesting and Model Validation

Backtesting is a crucial step in validating the effectiveness of an EMN strategy. By simulating the strategy’s performance on historical data, investors can assess its potential returns and identify any weaknesses in the model. This process also involves adjusting the model parameters and data inputs to optimize performance (CAIA).

Continuous Improvement

The dynamic nature of financial markets necessitates continuous improvement of EMN strategies. This involves updating models with new data, incorporating additional alternative data sources, and refining trading algorithms to maintain a competitive edge. By staying abreast of technological advancements and market trends, investors can ensure that their EMN strategies remain effective and profitable (Class Central).

Integration with Traditional Investment Strategies

While EMN strategies offer unique benefits, they can also complement traditional investment strategies by providing additional diversification and risk mitigation.

Complementing the 60/40 Portfolio

The traditional 60/40 portfolio, which allocates 60% to equities and 40% to bonds, can benefit from the inclusion of EMN strategies. By providing returns that are uncorrelated with the broader market, EMN strategies can enhance the overall risk-adjusted returns of the portfolio and reduce its vulnerability to market downturns (CAIA).

Enhancing Long-Biased Portfolios

EMN strategies can also complement long-biased portfolios by offering a hedge against market declines. By balancing long and short positions, these strategies can mitigate the impact of market volatility and provide a more stable return profile (Picton Mahoney).

In summary, building an equity market-neutral strategy using alternative data involves integrating innovative data sources, advanced machine learning models, and robust risk management practices. By doing so, investors can create strategies that offer compelling returns and diversification benefits, even in challenging market conditions.

Utilizing Alternative Data in EMN Strategy Advanced Data Integration Techniques

While previous sections have covered the basics of data collection and processing, this section delves deeper into advanced techniques for integrating alternative data into Equity Market Neutral (EMN) strategies. Unlike traditional data sources, alternative data such as satellite imagery, social media sentiment, and web traffic data require sophisticated integration methods to be effectively utilized in quantitative models.

Data Fusion: This technique involves combining multiple data sources to create a comprehensive dataset that enhances the predictive power of EMN strategies. For example, integrating satellite imagery with social media sentiment can provide a more nuanced view of consumer behavior and market trends (AlphaSense). Natural Language Processing (NLP): NLP is used to analyze unstructured text data from news articles and social media posts. By extracting sentiment and key themes, NLP can generate valuable insights that inform trading decisions. This approach is particularly useful for capturing market sentiment shifts that may not be reflected in traditional financial metrics (Aurum). Geospatial Analysis: Utilizing geolocation data from smartphones and other devices can offer insights into foot traffic patterns and consumer behavior. This data can be particularly valuable for retail and consumer goods sectors, providing a real-time view of market dynamics (AlphaSense). Signal Enhancement through Machine Learning

While previous sections discussed LSTM-based stock prediction, this section focuses on enhancing signal generation through advanced machine learning techniques. These methods leverage alternative data to improve the accuracy and reliability of trading signals in EMN strategies.

Ensemble Learning: By combining predictions from multiple machine learning models, ensemble learning can enhance the robustness of trading signals. This approach mitigates the risk of overfitting and improves the generalization of models to new data (OptimizDBA). Feature Engineering: Advanced feature engineering techniques can extract meaningful patterns from alternative data, such as identifying correlations between social media sentiment and stock price movements. These engineered features can significantly enhance the predictive power of machine learning models (SSRN). Deep Reinforcement Learning: This technique involves training models to make sequential decisions by interacting with a simulated market environment. By learning from past actions and outcomes, reinforcement learning models can optimize trading strategies in dynamic market conditions (Academia.edu). Risk Management Innovations

While previous sections have covered traditional hedging techniques, this section explores innovative risk management strategies that leverage alternative data to enhance EMN strategies.

Dynamic Risk Assessment: By continuously monitoring alternative data sources, such as news sentiment and social media trends, investors can dynamically assess and adjust risk exposures. This proactive approach allows for timely responses to emerging risks and opportunities (BlackRock). Volatility Prediction: Using machine learning models to predict market volatility based on alternative data can help investors better manage risk. For instance, analyzing social media sentiment can provide early warnings of potential market disruptions, allowing for preemptive risk mitigation (MDPI). Scenario Analysis: By simulating various market scenarios using alternative data, investors can evaluate the potential impact of different risk factors on their portfolios. This approach enables more informed decision-making and enhances the resilience of EMN strategies (OptimizDBA). Portfolio Optimization with Alternative Data

While previous sections have discussed portfolio construction, this section focuses on optimizing EMN portfolios using alternative data to achieve superior risk-adjusted returns.

Multi-Objective Optimization: This technique involves optimizing portfolios based on multiple criteria, such as maximizing returns while minimizing risk. By incorporating alternative data, investors can identify non-traditional factors that contribute to portfolio performance, such as consumer sentiment and environmental trends (Aurum). Adaptive Rebalancing: Utilizing real-time alternative data allows for adaptive portfolio rebalancing, ensuring that portfolios remain aligned with current market conditions. This approach enhances the agility of EMN strategies and improves their ability to capture short-term market opportunities (Academia.edu). Factor Analysis: By analyzing alternative data, investors can identify new factors that drive stock returns, such as brand loyalty or supply chain disruptions. Incorporating these factors into portfolio optimization models can enhance the predictive accuracy and performance of EMN strategies (SSRN). Continuous Strategy Refinement

While previous sections have emphasized the importance of continuous improvement, this section highlights specific strategies for refining EMN strategies using alternative data.

Feedback Loops: Establishing feedback loops that incorporate alternative data allows for the continuous refinement of trading models. By analyzing the outcomes of past trades, investors can identify areas for improvement and adjust their strategies accordingly (OptimizDBA). Data-Driven Insights: Leveraging alternative data for insights into market trends and consumer behavior can inform strategic adjustments to EMN strategies. This data-driven approach ensures that strategies remain relevant and effective in changing market environments (AlphaSense). Collaborative Innovation: Collaborating with data scientists and industry experts can foster innovation and enhance the development of EMN strategies. By sharing insights and expertise, investors can leverage the full potential of alternative data to drive superior performance (Aurum). Portfolio Construction and Risk Management in Equity Market Neutral Strategies Factor-Based Stock Selection

In equity market neutral (EMN) strategies, selecting the right stocks is crucial for achieving market neutrality and optimizing returns. Unlike traditional methods that rely heavily on historical price data, EMN strategies can benefit significantly from alternative data sources. These data sources include social media sentiment, satellite imagery, and web traffic analytics, which provide insights into market trends and company performance that are not captured by conventional financial metrics. For instance, social media sentiment analysis can reveal public perception and potential market movements before they are reflected in stock prices (CAIA).

Advanced Machine Learning Techniques

While LSTM models have been discussed in previous sections for stock prediction, this section focuses on other machine learning techniques that can enhance EMN strategies. Techniques such as random forests, gradient boosting machines, and support vector machines can be employed to capture complex, non-linear relationships in the data. These models can process large volumes of alternative data to identify patterns and predict stock movements more accurately. For example, random forests can be used to analyze a combination of financial ratios and alternative data to rank stocks based on their expected returns, thereby aiding in the construction of the long and short sides of the portfolio (Academia.edu).

Dynamic Portfolio Rebalancing

Dynamic portfolio rebalancing is a key component of risk management in EMN strategies. Unlike static rebalancing, which occurs at fixed intervals, dynamic rebalancing adjusts the portfolio in response to real-time market changes and new data inputs. This approach ensures that the portfolio remains aligned with the strategy’s objectives and market conditions. By integrating alternative data, such as real-time economic indicators and sentiment analysis, portfolio managers can make more informed decisions about when to rebalance the portfolio. This not only helps in maintaining market neutrality but also in capturing short-term market opportunities that may arise due to sudden shifts in market sentiment (MDPI).

Risk Metrics Beyond Traditional Measures

Traditional risk metrics like standard deviation and beta may not fully capture the risks associated with EMN strategies. Instead, advanced risk metrics such as Conditional Value-at-Risk (CVaR) and entropy pooling offer a more comprehensive view of potential risks. CVaR, for instance, measures the expected loss in the worst-case scenarios, providing a more robust assessment of tail risks. Entropy pooling, on the other hand, allows for the incorporation of subjective views and stress-testing scenarios, enabling a more nuanced risk management approach. These metrics help in identifying and mitigating risks that are not apparent through traditional measures, thereby enhancing the robustness of EMN strategies (Anton Vorobets).

Incorporating Sentiment Analysis

Sentiment analysis is an innovative tool for enhancing EMN strategies by providing insights into market psychology and investor behavior. By analyzing data from news articles, social media platforms, and financial reports, sentiment analysis can gauge the overall market mood and predict potential market movements. This information can be used to adjust the portfolio’s long and short positions to better align with market sentiment. For instance, a positive sentiment towards a particular sector may prompt an increase in long positions within that sector, while negative sentiment may lead to an increase in short positions. This proactive approach to portfolio management can improve the strategy’s performance and resilience in volatile markets (CAIA).

Integration of ESG Factors

Environmental, Social, and Governance (ESG) factors are increasingly being integrated into EMN strategies as a form of alternative data. ESG factors provide insights into a company’s sustainability practices and ethical considerations, which can influence long-term performance. By incorporating ESG data, EMN strategies can identify companies that are likely to outperform due to their strong ESG profiles. This not only enhances the strategy’s return potential but also aligns it with socially responsible investment practices. For example, companies with high ESG ratings may be favored for long positions, while those with poor ratings may be targeted for short positions, thereby aligning financial goals with ethical considerations (MDPI).

Continuous Monitoring and Adjustment

Continuous monitoring and adjustment are critical for maintaining the effectiveness of EMN strategies. This involves regularly reviewing the portfolio’s performance, risk exposures, and alignment with market conditions. By leveraging alternative data and advanced analytics, portfolio managers can identify emerging trends and potential risks in real-time. This enables them to make timely adjustments to the portfolio, such as reallocating assets or adjusting hedging strategies, to ensure that it remains on track to achieve its objectives. Continuous monitoring also facilitates the identification of underperforming assets, allowing for their timely replacement with better-performing alternatives (Academia.edu).

By integrating these advanced techniques and data sources, equity market neutral strategies can be significantly enhanced, providing better risk-adjusted returns and resilience in diverse market conditions. This approach not only leverages the latest advancements in data analytics and machine learning but also aligns with the growing demand for sustainable and responsible investment practices.

Conclusion

This research report outlines the construction of an Equity Market Neutral (EMN) strategy utilizing alternative data, emphasizing the integration of advanced machine learning models, sophisticated data collection techniques, and innovative risk management practices. The primary aim of EMN strategies is to exploit pricing inefficiencies while maintaining a neutral exposure to market movements. By leveraging alternative data sources such as social media sentiment, satellite imagery, and web traffic analytics, these strategies can enhance predictive accuracy and uncover alpha opportunities that traditional data might overlook. The report highlights the importance of data processing and signal generation, where techniques like Natural Language Processing (NLP) and geospatial analysis play crucial roles in extracting actionable insights from unstructured data.

The incorporation of machine learning models, particularly Long Short-Term Memory (LSTM) networks, is pivotal in handling time series data and predicting stock movements with high precision. These models, along with other techniques like ensemble learning and deep reinforcement learning, significantly improve the robustness and reliability of trading signals. Risk management is another critical component, with dynamic risk assessment and volatility prediction using alternative data providing a proactive approach to mitigating market risks. The report also underscores the benefits of integrating Environmental, Social, and Governance (ESG) factors, which align financial objectives with ethical investment practices, thereby enhancing the strategy’s appeal to socially responsible investors.

The findings suggest that the integration of alternative data and advanced analytics can significantly enhance the performance and resilience of EMN strategies, offering superior risk-adjusted returns even in volatile market conditions. The next steps involve continuous refinement of these strategies through feedback loops, data-driven insights, and collaborative innovation with data scientists and industry experts. By staying abreast of technological advancements and market trends, investors can ensure that their EMN strategies remain competitive and effective. This approach not only leverages cutting-edge data analytics but also aligns with the growing demand for sustainable and responsible investment practices, positioning EMN strategies as a valuable complement to traditional investment portfolios.

Conclusion: Fully automated generation (at least, this framework) does not seem yet on par with co-creation with a human expert, but a decent entrypoint for the layman.

Bayesian Networks for Business: Modeling Profit and Loss of a Cafe in Hong Kong

Mon, 14 Oct 2024 00:00:00 +0000

Bayesian Networks for Business: Modeling Profit and Loss of a Cafe in Hong Kong

Why Do Tai Hang’s Coffee Shops Keep Failing?

tl;dr Decreasing foot traffic, driven by a significant decline in the western expatriate population.

Context: The once vibrant Tai Hang neighborhood has seen a notable drop in its rich western population, as both anecdotal evidence, and to some extent our paper Mapping Hong Kong’s Financial Ecosystem studying the Hong Kong SFC public register demographics, suggest. This shift has heavily impacted local businesses, particularly coffee shops, struggling to attract enough patrons to stay afloat.

The bustling neighborhoods of Tin Hau and Tai Hang in Hong Kong have long been hotspots for coffee enthusiasts, drawing in strong foot traffic and Instagram influencers eager to capture the next aesthetic moment. However, since the COVID-19 pandemic, these once-thriving coffee shops have faced a significant decline. Despite beautifully designed interiors and quality coffee—often accompanied by excellent food—many of these businesses don’t last more than a year or two. It’s a perplexing trend, with new cafes continually popping up, only to close down shortly after. Why are so many coffee shops failing, and yet people keep trying their hand at running them?

This brings us to a key question: What does it take to break even in the cafe business? Can we estimate how much profit, or loss, a typical coffee shop would make in a year? How much risk is involved, and is it worth the effort to operate such a business?

In this blog, we’ll explore these questions by building a simple Bayesian network model to simulate the profit-and-loss (P&L) of a coffee shop over the course of a year. We’ll focus on key variables—like daily foot traffic, average bill size, rent, wages, and raw material costs—without diving into the complexities of setup costs (such as renovation, licenses, and administrative expenses). By simulating daily P&L across different scenarios, we aim to gain insights into the financial realities of running a cafe.

Although our model will remain simple for now, avoiding factors like customer reviews, seasonality, competition, and broader economic conditions, it provides a useful starting point. We also plan to gather feedback from F&B industry experts to validate whether our base assumptions align with current market conditions.

Ultimately, the simulations will show that operating a cafe is no easy feat, with potential annual P&L ranging between -3 million HKD and 2 million HKD. This blog will offer a clear view into the financial rollercoaster that is running a coffee shop in today’s Hong Kong.

import numpy as np
import pandas as pd
import networkx as nx
from tqdm import tqdm
import matplotlib.pyplot as plt

from pgmpy.factors.continuous import LinearGaussianCPD
from pgmpy.models import LinearGaussianBayesianNetwork

In this blog post, we will use pgmpy, a Bayesian networks library.

Using pgmpy, we will define continuous Conditional Probability Distributions (CPDs) and model the key relationships between variables that drive the profit-and-loss dynamics of a cafe. This will enable us to simulate and better understand the financial outcomes of running a coffee shop under different conditions.

In our model, we define key variables that influence the daily profit of a coffee shop. The number of daily visitors, or FootTraffic, directly impacts DailySales, as more patrons generally translate to higher revenue. DailySales, in turn, affects two key cost drivers: RawMaterialCosts (the expense of ingredients and consumables) and Profit. The higher the sales, the more materials are needed, increasing costs. Additionally, Wages (fixed costs for staff) and Rent (fixed operating costs) both influence the Profit. By linking these variables, we model how changes in foot traffic, sales, and costs affect the cafe’s profitability on a daily basis.

We define the model in the cell below:

def build_model(average_bill=60, average_traffic=100, daily_rent=2000, daily_wage=1200):
    
    model = LinearGaussianBayesianNetwork([
        ('FootTraffic', 'DailySales'),
        ('DailySales', 'RawMaterialCosts'),
        ('DailySales', 'Profit'),
        ('RawMaterialCosts', 'Profit'),
        ('Wages', 'Profit'),
        ('Rent', 'Profit'),
    ])

    # Define the CPD for FootTraffic (independent variable)
    cpd_foot_traffic = LinearGaussianCPD('FootTraffic', [average_traffic], (0.1 * average_traffic)**2)

    # Define the CPD for DailySales as a function of FootTraffic
    cpd_daily_sales = LinearGaussianCPD(
        'DailySales',
        [0, average_bill],
        (0.1 * average_bill)**2,
        ['FootTraffic']
    )

    # Define the CPD for RawMaterialCosts as a function of DailySales
    cpd_raw_material_costs = LinearGaussianCPD('RawMaterialCosts', [100, 0.4], 20, ['DailySales'])

    cpd_wages = LinearGaussianCPD('Wages', [daily_wage], 0)  # Fixed cost for wages

    cpd_rent = LinearGaussianCPD('Rent', [daily_rent], 0)  # Fixed cost for rent

    # Define the CPD for Profit as a function of DailySales, RawMaterialCosts, Wages, and Rent
    cpd_profit = LinearGaussianCPD(
        'Profit', [0, 1, -1, -1, -1], 10, ['DailySales', 'RawMaterialCosts', 'Wages', 'Rent'])

    # Add CPDs to the model
    model.add_cpds(
        cpd_foot_traffic,
        cpd_daily_sales, 
        cpd_raw_material_costs, 
        cpd_wages, 
        cpd_rent,
        cpd_profit
    )

    assert model.check_model()

    return model

In the cell above, we build a Bayesian network model that simulates the daily profit of a coffee shop based on several variables: FootTraffic, DailySales, RawMaterialCosts, Wages, Rent, and Profit. Let’s break down the model and the logic behind it.

Network Structure:

The model connects the variables as follows:

FootTraffic → DailySales: The number of people visiting the cafe directly affects the daily sales. We use FootTraffic as an input variable to predict DailySales.
DailySales → RawMaterialCosts & Profit: The sales determine the raw material costs (since higher sales require more ingredients) and also directly contribute to profit.
RawMaterialCosts, Wages, Rent → Profit: The three cost factors—raw materials, wages, and rent—reduce profit, acting as outflows from the revenue generated by daily sales.

Conditional Probability Distributions (CPDs):

We use LinearGaussianCPDs to describe the relationships between these variables. Each CPD defines how one variable depends on another (or stays constant, in the case of fixed costs):

FootTraffic: This is treated as an independent variable. We model it with a mean value (average_traffic) and a variance, representing the fluctuation in the number of visitors per day. In formula terms:

$\text{FootTraffic} \sim \mathcal{N}\left(\mu = \text{average_traffic}, \sigma^2 = \left(0.1 \times \text{average_traffic}\right)^2\right)$

DailySales: This is modeled as a function of foot traffic. The more visitors, the more sales. The average bill per customer is represented by average_bill. In formula terms:

$\text{DailySales} = 0 + \text{FootTraffic} \times \text{average_bill} + \epsilon, \quad \epsilon \sim \mathcal{N}\left(0, \left(0.1 \times \text{average_bill}\right)^2\right)$

RawMaterialCosts: The cost of raw materials is modeled as a percentage of daily sales, reflecting the idea that a fraction of sales goes towards covering ingredient costs. For instance, in this case, 40% of sales goes to raw materials, with a base cost of 100 HKD per day:

$\text{RawMaterialCosts} = 100 + 0.4 \times \text{DailySales} + \epsilon, \quad \epsilon \sim \mathcal{N}(0, 20)$

Wages & Rent: These are fixed daily costs, represented with no variability, as modeled by:

$\text{Wages} = \text{daily_wage}, \text{Rent} = \text{daily_rent}$

Profit: Finally, we calculate profit as the difference between revenue and costs. In formula terms, the profit is modeled as:

$\text{Profit} = \text{DailySales} - \text{RawMaterialCosts} - \text{Wages} - \text{Rent} + \epsilon, \quad \epsilon \sim \mathcal{N}(0, 10)$

model = build_model()

# Convert the Bayesian Network to a Joint Gaussian Distribution (JGD) for inference
jgd = model.to_joint_gaussian()

# Extract the mean vector and covariance matrix from the JGD
mean = jgd[0]
covariance = jgd[1]

In the cell above:

First, we call the build_model() function, which constructs our Bayesian network for the cafe’s profit and loss.
Then, we convert this Bayesian network into a Joint Gaussian Distribution (JGD) using to_joint_gaussian(). This step is essential because it transforms the network into a form that allows us to perform inference.
Finally, we extract two key components from the JGD:
- The mean vector, representing the expected values for all variables.
- The covariance matrix, representing the relationships (dependencies) between variables, particularly how changes in one variable affect others.

In the plot below, we visualize the Bayesian Network structure of our cafe profit model:

graph = nx.DiGraph()
graph.add_nodes_from(model.nodes())
graph.add_edges_from(model.edges())

plt.figure(figsize=(10, 8))
pos = nx.spring_layout(graph, scale=0.01, seed=45)
nx.draw(graph, pos, with_labels=True, node_size=4000, node_color="skyblue", 
        font_size=10, font_weight="bold", arrows=True, arrowsize=20, edge_color="gray")
plt.title("Bayesian Network of Cafe Profit Model", size=14)
plt.show()

pd.DataFrame(
    mean,
    index=["FootTraffic", "Wages", "Rent", "DailySales", "RawMaterialCosts", "Profit"]
).reset_index().rename(columns={"index": "variable", 0: "mean"})

	variable	mean
0	FootTraffic	100.0
1	Wages	1200.0
2	Rent	2000.0
3	DailySales	6000.0
4	RawMaterialCosts	2500.0
5	Profit	300.0

The simulation below models the profit-and-loss (PnL) of a café over a year, based on varying levels of daily foot traffic and average customer spending (bill size). Here’s a breakdown of the logic and what each part is doing:

1. Traffic and Bill Size Simulation

The simulation explores a range of foot traffic levels (traffic = [10 * i for i in range(1, 12)]) and average customer bill sizes (bill = range(40, 71)). For each combination of traffic and bill size, a Bayesian network model is built to represent the relationships between key variables like foot traffic, daily sales, raw material costs, wages, rent, and profit.

2. Model Creation

For each combination of foot traffic and bill size:

The model is built using the build_model() function, which sets the relationships between the variables (e.g., how foot traffic impacts daily sales, how daily sales affect profit).
This model is then converted into a joint Gaussian distribution (jgd = model.to_joint_gaussian()), which allows for inference across the network of variables.

3. Daily Profit Simulation

Once the model is set up, a Monte Carlo simulation is run (NB_SIMU = 1000). For each simulation:

A year’s worth of daily profit is simulated by generating an observed foot traffic level for each day, drawn from a normal distribution around the specified average foot traffic (np.random.normal(average_traffic, average_traffic * 0.1)).
Using the observed foot traffic and the conditional relationships between variables (captured in the covariance matrix of the joint Gaussian distribution), the daily sales and profit are calculated based on the observed traffic.

4. Aggregation of Results

For each simulation, the cumulative profit over the year is recorded, and then averaged across all simulations for each combination of foot traffic and bill size. This results in an estimate of the mean annual profit (PnL) for a café given different levels of foot traffic and average bill size.

mean_year_pnl = []

traffic = [10 * i for i in range(1, 12)]
for average_traffic in tqdm(traffic):
    
    mean_year_pnl_per_traffic = []

    bill = range(40, 71)
    for average_bill in bill:

        model = build_model(
            average_bill=average_bill,
            average_traffic=average_traffic,
            daily_rent=1500,
        )

        # Convert to Joint Gaussian Distribution (for inference)
        jgd = model.to_joint_gaussian()


        # Extract the mean and covariance matrix from the joint Gaussian distribution
        mean = jgd[0]
        covariance = jgd[1]


        NB_SIMU = 1000
        all_daily_pnl = []
        for simu in range(NB_SIMU):
            daily_pnl = []
            dates = pd.date_range("2024-01-01", "2025-01-01")
            for date in dates:

                # Observed foot traffic
                observed_foot_traffic = np.random.normal(average_traffic, average_traffic * 0.1)

                # Partition the joint distribution into blocks for conditioning
                mean_daily_sales = mean[3]      # Mean of DailySales
                mean_profit = mean[5]           # Mean of Profit

                # Extract variances and covariances needed for calculations
                cov_daily_sales = covariance[3, 3]  # Variance of DailySales
                cov_profit = covariance[5, 5]       # Variance of Profit

                # Extract covariances with observed variables
                cov_daily_sales_foot_traffic = covariance[0, 3]   # Covariance between DailySales and FootTraffic
                cov_profit_daily_sales = covariance[5, 3]         # Covariance between Profit and DailySales

                # Calculate conditional mean and variance for DailySales given the observed values
                conditional_mean_daily_sales = (
                    mean_daily_sales + 
                    (cov_daily_sales_foot_traffic * (observed_foot_traffic - mean[0]) / covariance[0, 0])
                )

                # Calculate conditional variance for DailySales
                conditional_variance_daily_sales = (
                    cov_daily_sales - 
                    (cov_daily_sales_foot_traffic ** 2 / covariance[0, 0])
                )

                # Now calculate the conditional mean and variance for
                # Profit given the observed values of DailySales
                conditional_mean_profit_given_daily_sales = (
                    mean_profit + 
                    cov_profit_daily_sales * (conditional_mean_daily_sales - mean_daily_sales) / cov_daily_sales
                )

                conditional_variance_profit_given_daily_sales = (
                    cov_profit - (cov_profit_daily_sales ** 2) / cov_daily_sales
                )

                daily_pnl.append(conditional_mean_profit_given_daily_sales)

            all_daily_pnl.append(daily_pnl)
    
        mean_year_pnl_per_traffic.append(pd.DataFrame(all_daily_pnl).cumsum().iloc[-1].mean())
        
    mean_year_pnl.append(mean_year_pnl_per_traffic)

100%|███████████████████████████████████████████| 11/11 [04:04<00:00, 22.25s/it]

df_mean_year_pnl = pd.DataFrame(mean_year_pnl, index=traffic, columns=bill)

How to Interpret the Simulation

Traffic Impact: By varying foot traffic from low to high, the simulation shows how different levels of customer footfall influence the café’s annual profit. Lower traffic may result in negative profits (losses), while higher traffic might lead to profitability.
Bill Size Sensitivity: The model also explores the impact of average customer spending (the bill size). A small increase in average bill size could lead to higher profit margins since fixed costs (rent, wages) remain constant, and the additional revenue directly boosts profitability.
Annual Profit Ranges: For each scenario of foot traffic and bill size, you’ll see the range of possible profit outcomes, helping to assess how sensitive the café’s financial performance is to these key variables.

plt.figure(figsize=(12, 8))
plt.pcolormesh(df_mean_year_pnl, cmap='RdYlGn')
plt.grid(True, which='both', color='lightgray', linestyle='--', linewidth=0.5)
plt.xticks(range(len(bill)), bill, rotation=90, fontsize=12)
plt.yticks(range(len(traffic)), traffic, rotation=90, fontsize=12)
plt.colorbar()
plt.xlabel("Average bill per patron (in HKD)", size=14)
plt.ylabel("Average number of patrons in a day", size=14)
plt.title("Yearly profit (HKD)", size=14)
plt.tight_layout()

Quick Comment on the Plot

The plot visualizes the yearly profit of a café as a function of average foot traffic (number of patrons per day) and average bill size (spending per customer). Each cell represents the estimated profit based on the combination of these two factors, with color intensity indicating profit levels.

Key observations:

Low foot traffic (bottom rows) generally results in negative profits, regardless of the bill size, indicating that a minimum customer base is essential to cover fixed costs like rent and wages.
Higher foot traffic (top rows) leads to a positive profit zone, especially as the average bill size increases.
Profit Sensitivity: There is a clear transition from loss to profit as the average number of patrons and their spending increase, highlighting that both high traffic and a sufficient average bill are crucial for the café’s success.

This plot helps identify the break-even points, where running the café becomes profitable, and provides an intuitive visual guide for understanding how small changes in traffic or bill size affect overall profitability.

plt.figure(figsize=(12, 6))
foot_traffic = 90
df_mean_year_pnl.loc[foot_traffic].plot(marker='o', markersize=6, color='blue', lw=2, label='Profit')
plt.axhline(0, color='red', linestyle='--', lw=2, label='Break-even')
plt.grid(True, which='both', linestyle='--', linewidth=0.5, color='gray')
plt.xlabel("Average bill per patron (in HKD)", size=14)
plt.ylabel("Yearly profit (HKD)", size=14)
plt.title(f"Yearly profit in HKD (assuming {foot_traffic} daily patrons)", size=14)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.legend(loc='upper left', fontsize=12)
plt.tight_layout()
plt.show()

Brief Comment on the Plot

This plot illustrates the projected yearly profit of the café for a foot traffic level of 90 daily patrons, depending on the average spending per customer (bill size).

The red dashed line represents the break-even point, where profit is zero.
As we can see, with lower average bills, the café operates at a loss. However, once the average bill surpasses approximately HKD 52, the café crosses the break-even threshold and starts generating profit.
The plot shows the sensitivity of profitability to the bill size: even small increases in the average bill lead to significant improvements in yearly profit once the business crosses the break-even point.

This graph provides valuable insights into how bill size impacts the café’s financial performance, showing that profitability is highly dependent on maintaining a sufficiently high average spend per customer.

plt.figure(figsize=(12, 6))
avg_bill_patron_1 = 55
avg_bill_patron_2 = 65

# Plot the curves with different styles for better distinction
df_mean_year_pnl.T.loc[avg_bill_patron_1].plot(
    label=f"Average bill / patron: HKD {avg_bill_patron_1}", linestyle='-', marker='o', markersize=6, lw=2)
df_mean_year_pnl.T.loc[avg_bill_patron_2].plot(
    label=f"Average bill / patron: HKD {avg_bill_patron_2}", linestyle='--', marker='s', markersize=6, lw=2)

# Add the break-even line
plt.axhline(0, color='red', linestyle='--', lw=2, label='Break-even')

# Add gridlines and labels
plt.grid(True, which='both', linestyle='--', linewidth=0.5, color='gray')
plt.xlabel("Average number of patrons in a day", size=14)
plt.ylabel("Yearly profit (HKD)", size=14)
plt.title("Yearly profit in HKD (depending on number of daily patrons)", size=14)

# Customize ticks and legend
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.legend(loc='upper left', fontsize=12)
plt.tight_layout()
plt.show()

Brief Comment on the Plot

This plot visualizes the yearly profit of the café based on the number of daily patrons for two different average bill amounts: HKD 55 and HKD 65.

The solid line represents the yearly profit for an average bill of HKD 55, while the dashed line represents an average bill of HKD 65.
The red dashed line marks the break-even point, where the profit equals zero.
As expected, a higher average bill significantly boosts the profitability of the café, especially when the daily foot traffic increases.
For both bill amounts, the café operates at a loss at lower foot traffic levels, but as the number of daily patrons rises, profitability improves, with the break-even point being reached earlier for the HKD 65 bill compared to the HKD 55 bill.

Of course, pricing is competitive, and you may lose patrons by increasing price… which is not modeled at all here.

Overall, this simulation provides insights into the break-even points and profitability of a small café, highlighting how critical customer traffic and average spending are to the business’s financial health.

Simulation for 1 year of business, given a set of parameters

This final simulation runs multiple trajectories (1,000 simulations) of daily profit over the course of one year, given a specific set of parameters:

Average foot traffic: 80 patrons per day
Average bill per patron: HKD 59
Daily rent: HKD 1,500
Daily wages: HKD 1,200

Explanation of the Process

For each simulation, daily profit is computed based on observed daily foot traffic, which fluctuates around the set average (80 patrons), with variability of 20% (i.e., foot traffic is drawn from a normal distribution centered on 80 with a standard deviation of 16).
Daily profit is computed through the Bayesian Network, which conditions profit on variables such as foot traffic and daily sales, using the Joint Gaussian Distribution to account for dependencies between the variables.
The cumulative yearly profit is then calculated by summing up the daily profits for each simulation.

# simu of a given year for a given set of parameters:

FOOT_TRAFFIC = 80
model = build_model(
    average_bill=59,
    average_traffic=FOOT_TRAFFIC,
    daily_rent=1500,
    daily_wage=1200,
)

# Convert to Joint Gaussian Distribution (for inference)
jgd = model.to_joint_gaussian()


# Extract the mean and covariance matrix from the joint Gaussian distribution
mean = jgd[0]
covariance = jgd[1]

all_daily_pnl = []
NB_SIMU = 1000
for simu in tqdm(range(NB_SIMU)):
    daily_pnl = []
    dates = pd.date_range("2024-01-01", "2025-01-01")
    for date in dates:

        # Observed foot traffic
        observed_foot_traffic = np.random.normal(FOOT_TRAFFIC, 0.2 * FOOT_TRAFFIC)

        # Partition the joint distribution into blocks for conditioning
        mean_daily_sales = mean[3]      # Mean of DailySales
        mean_profit = mean[5]           # Mean of Profit

        # Extract variances and covariances needed for calculations
        cov_daily_sales = covariance[3, 3]  # Variance of DailySales
        cov_profit = covariance[5, 5]       # Variance of Profit

        # Extract covariances with observed variables
        cov_daily_sales_foot_traffic = covariance[0, 3]   # Covariance between DailySales and FootTraffic
        cov_profit_daily_sales = covariance[5, 3]         # Covariance between Profit and DailySales

        # Calculate conditional mean and variance for DailySales given the observed values
        conditional_mean_daily_sales = (
            mean_daily_sales + 
            (cov_daily_sales_foot_traffic * (observed_foot_traffic - mean[0]) / covariance[0, 0])
        )

        # Calculate conditional variance for DailySales
        conditional_variance_daily_sales = (
            cov_daily_sales - 
            (cov_daily_sales_foot_traffic ** 2 / covariance[0, 0])
        )

        # Now calculate the conditional mean and variance for Profit given the observed values of DailySales
        conditional_mean_profit_given_daily_sales = (
            mean_profit + 
            cov_profit_daily_sales * (conditional_mean_daily_sales - mean_daily_sales) / cov_daily_sales
        )

        conditional_variance_profit_given_daily_sales = (
            cov_profit - (cov_profit_daily_sales ** 2) / cov_daily_sales
        )

        daily_pnl.append(conditional_mean_profit_given_daily_sales)

    all_daily_pnl.append(daily_pnl)

100%|█████████████████████████████████████| 1000/1000 [00:00<00:00, 1443.14it/s]

The histogram shows the distribution of cumulative yearly profits across all simulations. It helps assess the variability and risk of the business:

The center of the distribution tells us the most likely range of outcomes.
The spread (variance) reflects the financial uncertainty the café might face due to fluctuations in foot traffic and other factors.

pd.DataFrame(all_daily_pnl).cumsum().iloc[-1].hist()

The trajectory plot shows the evolution of cumulative profit throughout the year for each simulation:

It visualizes how profits evolve day-by-day, highlighting the range of possible trajectories.
We observe significant variations, but overall patterns can emerge, such as the tendency to move into positive or negative profitability over time.

pd.DataFrame(all_daily_pnl).cumsum().plot(legend=False)

pd.DataFrame(all_daily_pnl).cumsum().iloc[-1].describe()

count      367.000000
mean     32584.652923
std      19024.309440
min     -24541.186664
25%      19872.687208
50%      32301.141206
75%      46470.777014
max      90926.336985
Name: 999, dtype: float64

Key Takeaways

Profitability Uncertainty: The simulations indicate that while the café has potential to be profitable over the course of the year, variability in foot traffic creates uncertainty. The spread in both the histogram and trajectory plot shows the risk of losses in some scenarios, although the average outcome leans towards profitability.
Break-even Point: Across the majority of simulations, the café does break even, but the risk of underperformance due to lower-than-expected foot traffic remains present.

Conclusion of the Study

In this simulation-based study of a café’s profit-and-loss model, we explored the business performance under daily fluctuations of key factors such as foot traffic and sales. The Bayesian network framework allowed us to capture and model the interdependencies between these variables, providing valuable insights into how the café is likely to perform over time.

Key insights include:

The café’s profitability is highly sensitive to fluctuations in daily foot traffic, where even moderate changes can lead to significant profit variability.
Incremental increases in the average bill per patron have a noticeable impact on the overall profitability, showcasing the importance of pricing.
While the risk of financial underperformance remains, the model shows that under normal conditions, the café has a good chance of maintaining profitability.

Potential Follow-ups, Improvements, and Next Steps

Foot Traffic Seasonality: Incorporating seasonality into foot traffic would better capture real-world patterns, allowing us to simulate peak periods such as holidays or tourist seasons and to reflect the potential impact of these cycles on profits.
Uncertainty Reduction: To improve the accuracy of the simulations, gathering more data from real-world café operations would help refine key parameters like average bill size, customer flow, and fixed costs, reducing the model’s uncertainty.
Operational Costs Variability: Modeling fluctuations in operational costs, such as changes in raw material prices or labor costs, would provide a deeper understanding of how these factors impact profitability, especially during economic shifts.
Marketing Impact: Analyzing the potential effect of marketing initiatives on foot traffic and sales could provide insights into how different promotional efforts may enhance profitability.

In future work, incorporating these elements would enable a more holistic view of the café’s operations and give clearer forecasts of profitability under varied business conditions.

Preparing for ICML 2024: Main themes

Sat, 22 Jun 2024 00:00:00 +0000

Preparing for ICML 2024: Main themes

I did not have much time to prepare this year as quite busy with various things; And the conference is already next week! So be it.

Before jumping into the cluster-based “main themes”, I just highlight a couple of interesting papers I had time to read over the past couple of week-ends:

GPTSwarm: Language Agents as Optimizable Graphs

A unified framework for Large Language Model (LLM) agents using computational graphs. It introduces automatic graph optimizers for refining node-level prompts and improving agent orchestration. By representing agents as graphs, the framework facilitates the integration and enhancement of various LLM agents through node and edge optimization. Experimental results show the effectiveness of this approach in developing and automatically improving LLM agents. The framework aims to streamline and enhance the performance of language agents in various applications.

Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits

Critically examines the use of Data Shapley for data selection, highlighting its inconsistent performance across various settings. The authors propose a hypothesis testing framework showing that without specific utility function constraints, Data Shapley may perform no better than random selection. They identify a class of utility functions where Data Shapley excels and propose a heuristic for predicting its effectiveness.

Unified Training of Universal Time Series Forecasting Transformers

Presents a universal transformer model for time series forecasting, aiming to handle diverse datasets with varying characteristics. The authors introduce a unified training framework that enhances model adaptability and performance across different types of time series data.

SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention

SAMformer, a model designed to enhance the performance of transformers in time series forecasting. SAMformer incorporates Sharpness-Aware Minimization (SAM) to navigate the loss landscape more effectively, preventing the model from falling into suboptimal local minima. Additionally, it employs channel-wise attention to improve focus on relevant features.

REPOFORMER: Selective Retrieval for Repository-Level Code Completion

Introduces a framework that uses selective retrieval-augmented generation (RAG) to improve code completion tasks. Repoformer evaluates whether retrieval will enhance performance before retrieving context, improving efficiency and accuracy. It employs self-supervised learning for robust code completion and self-evaluation.

Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam Generation

Introduces a novel method for assessing the task-specific accuracy of Retrieval-Augmented Large Language Models (RAG). This evaluation is performed through automatically generated synthetic exams composed of multiple-choice questions derived from the task’s document corpus. The approach leverages Item Response Theory (IRT) to estimate the quality and informativeness of the exams, iteratively refining them by eliminating less informative questions. The method demonstrates its effectiveness across various open-ended question-answering tasks, providing insights into factors impacting RAG performance, such as retrieval algorithms and model size.

Chain of Code: Reasoning with a Language Model-Augmented Code Emulator

A framework that enhances language model (LM) reasoning by combining code execution and LM-based code emulation. CoC generates code or pseudocode for problem-solving and uses an LM to simulate execution when code can’t be run by an interpreter. This approach outperforms traditional methods like Chain of Thought in complex reasoning tasks.

Now, let’s turn to the (basic) analysis of the “main themes”.

In anticipation of ICML 2024, I have downloaded the titles and abstracts of the accepted papers and conducted some basic DS/NLP analysis, including embeddings, UMAP, and prompting to uncover the main themes, and also quickly identify the papers of interest (to me).

Below are 10 clusters (themselves split into sub-clusters) along with links to the papers I plan to read carefully. If there is any associated GitHub code available, I may also experiment with it.

ICML 2024 Abstracts:

Cluster id: 0

Federated and Differentially Private Learning on Heterogeneous Data

Federated Learning is decentralized learning where one sends the model (or parts of it) to several devices so it trains locally on their data, and one gets back model parameters or gradients to update the overall model. Doing so, it helps preserves privacy, data security. It also allows for greater personalization of the model predictions by fine-tuning on a given user’s device. It can also be a way to efficiently train models by parallelizing the training process across very many machines.

Potential application: Could be used in retail banking to train a ‘global’ fraud detection model across many banks and geographies without the need to share customers’ transactions data.

Differential Privacy is a framework to ensure that the output of a model does not reveal too much information about any single data point on which the model was trained. In applications where personal information of individuals is a concern, it can be used to ensure that their data won’t be leaked by the model.

Potential application for forecasting: Differential Privacy controlled noise addition mechanism can make the model more robust (regularization).

1. Federated Learning Enhancements and Efficiency

“Accelerating Heterogeneous Federated Learning with Closed-form Classifiers”
“A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization”
“FedMBridge: Bridgeable Multimodal Federated Learning”
“Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning”
“Balancing Similarity and Complementarity for Federated Learning”
“AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning”
“FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees”
“Recurrent Early Exits for Federated Learning with Heterogeneous Clients”
“Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes”
“FedTYPE: Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning”
“Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors”
“FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error”
“FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models”
“FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler”
“Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization”
“SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding”
“Federated Continual Learning via Prompt-based Dual Knowledge Transfer”
“FEDU: Federated Unsupervised Data Augmentation for Improving Generalization”
“Federated Optimization with Doubly Regularized Drift Correction”
“Federated Learning with Dynamic Scheduling and Balancing Timing Constraints”
“Profiling: Efficient Optimization of Neural Networks with Federated Learning”
“Harmonizing Generalization and Personalization in Federated Prompt Learning”
“FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data”
“Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning”

2. Differential Privacy and Privacy-Preserving Machine Learning

“Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining”
“Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent”
“Differentially Private Representation Learning via Image Captioning”
“Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers”
“Privacy-Preserving Embedding via Look-up Table Evaluation with Fully Homomorphic Encryption”
“Privacy Profiles for Private Selection”
“A New Theoretical Perspective on Data Heterogeneity in Federated Averaging”
“Rethinking DP-SGD in Discrete Domain: Exploring Logistic Distribution in the Realm of signSGD”
“Better Locally Private Sparse Estimation Given Multiple Samples Per User”
“Neural Collapse meets Differential Privacy: Curious behaviors of NoisyGD with Near-Perfect Representation Learning”
“Proactive DP: A Multiple Target Optimization Framework for DP-SGD”
“Split-and-Denoise: Protect large language model inference with local differential privacy”
“Local Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy”
“Provable Privacy with Non-Private Pre-Processing”
“Differentially Private Bias-Term Fine-tuning of Foundation Models”
“Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning”
“Making old things new: a unified algorithm for differentially private clustering”
“PID: Prompt-Independent Data Protection Against Latent Diffusion Models”
“Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems”
“Tuning-free Estimation and Inference of Cumulative Distribution Function under Local Differential Privacy”
“Identifying Optimal Privacy Parameters for the Best Accuracy under $(\epsilon,\delta)$-DP”
“Differentially Private Domain Adaptation with Theoretical Guarantees”
“DPZero: Private Fine-Tuning of Language Models without Backpropagation”

3. Membership Inference Attacks and Model Robustness

“Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss”
“How Private are DP-SGD Implementations?”
“Verifying Machine Unlearning is Fragile”
“Recovering Labels from Local Updates in Federated Learning”
“Low-Cost High-Power Membership Inference Attacks”
“Understanding Robustness in Pipeline-Parallelism-Based Decentralized Training”
“Privacy Backdoors: Stealing Data with Corrupted Pretrained Models”
“Profile Reconstruction from Private Sketches”
“Membership Inference Attacks on Diffusion Models via Quantile Regression”
“Techniques for Private Membership Inference in Different Learning Models”
“Auditing Private Prediction”
“Differentially Private Inference for Neural Networks without Training Data”

4. Novel Approaches in Federated and Privacy-Preserving Learning

“COALA: A Practical and Vision-Centric Federated Learning Platform”
“Shifted Interpolation for Differential Privacy”
“MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis”
“FairProof: Confidential and Certifiable Fairness for Neural Networks”
“ViP: A Differentially Private Foundation Model for Computer Vision”
“Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers”
“Byzantine-Resilient Federated Learning: Impact of Client Subsampling and Local Updates”
“Beyond the Calibration Point: Mechanism Comparison in Differential Privacy”
“FedSaC: Federated Similarity and Complementarity Learning”
“Collaborative Differentially Private Personalization via Generative Data”
“Differentially Private Sum-Product Networks”
“Byzantine-Resilient Federated Learning with Serverless Aggregation”

5. Optimizations and Algorithms for Learning in Distributed Systems

“Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms”
“Adaptive Group Personalization for Federated Mutual Transfer Learning”
“Federated Representation Learning in the Under-Parameterized Regime”
“Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy”
“Causally Motivated Personalized Federated Learning with Shortcut-Averse Information-Theoretic Regularization”
“Decomposable Submodular Maximization in Federated Setting”
“Sequential Decision-Making in Federated Machine Learning”
“PrE-Text: Training Text-based Models Using Proximal Federated Evolution”
“Improved Modeling of Federated Data Using Mixtures-of-Dirichlet-Multinomials”

Cluster id: 1

Innovative Methods in Vision and Language Models: Enhancing Robustness, Self-Supervised Learning, and Domain Adaptation

1. Multimodal Learning and Vision-Language Alignment

“Sparse-to-dense Multimodal Image Registration via Multi-Task Learning”
“Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model”
“MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization”
“CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers”
“STELLA: Continual Audio-Video Pre-training with SpatioTemporal Localized Alignment”
“Libra: Building Decoupled Vision System on Large Language Models”
“FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction”
“Language-Driven Cross-Modal Classifier for Zero-Shot Multi-Label Image Recognition”
“VideoPoet: A Large Language Model for Zero-Shot Video Generation”
“Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization”
“NExT-GPT: Any-to-Any Multimodal LLM”

2. Vision Transformers and Efficient Architectures

“Enhancing Vision Transformer: Amplifying Non-Linearity in Feedforward Network Module”
“Exploring Training on Heterogeneous Data with Mixture of Low-rank Adapters”
“Visual Transformer with Differentiable Channel Selection: An Information Bottleneck Inspired Approach”
“Sparse Model Inversion: Efficient Inversion of Vision Transformers for Data-Free Applications”
“SCoRe: Submodular Combinatorial Representation Learning”
“Representation Surgery for Multi-Task Model Merging”
“Sparse-to-dense Multimodal Image Registration via Multi-Task Learning”

3. Self-Supervised Learning and Dataset Distillation

“Matrix Information Theory for Self-Supervised Learning”
“Sharpness-Aware Data Generation for Zero-shot Quantization”
“Improving Interpretation Faithfulness for Vision Transformers”
“Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion”
“Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features”
“Representation Surgery for Multi-Task Model Merging”
“Autoencoding Conditional Neural Processes for Representation Learning”
“One for All: A Universal Generator for Concept Unlearnability via Multi-Modal Alignment”

4. Image and Video Generation

“Creative Text-to-Audio Generation via Synthesizer Programming”
“Fast Text-to-3D-Aware Face Generation and Manipulation via Direct Cross-modal Mapping and Geometric Regularization”
“Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts”
“Memory Consolidation Enables Long-Context Video Understanding”
“Compositional Text-to-Image Generation with Dense Blob Representations”
“MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions”
“Genie: Generative Interactive Environments”
“Image Fusion via Vision-Language Model”
“High-Order Contrastive Learning with Fine-grained Comparative Levels for Sparse Ordinal Tensor Completion”

5. Few-Shot Learning and Domain Adaptation

“Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method”
“Learning Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition”
“DMTG: One-Shot Differentiable Multi-Task Grouping”
“Meta Evidential Transformer for Few-Shot Open-Set Recognition”
“Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining”
“Compositional Few-Shot Class-Incremental Learning”
“One Meta-tuned Transformer is What You Need for Few-shot Learning”

6. Adversarial Robustness and Model Security

“Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness”
“Robust CLIP: Unsupervised Adversarial Fine-tuning of Vision Embeddings for Robust Large Vision-Language Models”
“ON Mechanistic Knowledge Localization in Text-to-Image Generative Models”
“Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation”
“Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image Restoration”
“Sharpness-Aware Data Generation for Zero-shot Quantization”
“Mapping the Multiverse of Latent Representations”

7. Explainability and Interpretability

“Disentanglement Learning via Topology”
“Gradient-based Visual Explanation for Transformer-based CLIP”
“Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models”
“From Vision to Audio and Beyond: A Unified Model for Audio-Visual Representation and Generation”
“InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation”
“Towards generalizable particle picking in Cryo-EM images by leveraging Masked AutoEncoders”
“Understanding Retrieval-Augmented Task Adaptation for Vision-Language Models”

8. Medical Imaging and Bioinformatics

“Scale-Free Image Keypoints Using Differentiable Persistent Homology”
“Enhancing Single-Cell VAE Latent Space via Semi-Supervision”
“Multimodal Prototyping for cancer survival prediction”
“A Touch, Vision, and Language Dataset for Multimodal Alignment”
“Mixed-Feature Selection in Histopathology Images”
“SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals”
“PLUTO: Pathology-Universal Transformer”
“X-Oscar: A Progressive Framework for High-quality Text-guided 3D Animatable Avatar Generation”

9. Image Quality and Compression

“Residual Quantization with Implicit Neural Codebooks”
“Compress Clean Signal from Noisy Raw Image: A Self-Supervised Approach”
“OptoDex: Gap-Free Object Recognition Through Adversarial Erasers”
“Hyperfields: Towards Zero-Shot Generation of NeRFs from Text”
“Hyperbolic Active Learning for Semantic Segmentation under Domain Shift”
“A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture”

10. Miscellaneous (Various Novel Methods and Applications)

“Receptive Fields As Experts in Convolutional Neural Architectures”
“Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices”
“Bootstrap AutoEncoders With Contrastive Paradigm for Self-supervised Gaze Estimation”
“IIANet: An Intra- and Inter-Modality Attention Network for Audio-Visual Speech Separation”
“Mapping the Multiverse of Latent Representations”
“Tell, Don’t Show: Language Guidance Eases Transfer Across Domains in Images and Videos”
“EVMerge: Language-Aware Vision Merging Under Uncertainty”

Cluster id: 2

Advanced Techniques and Applications in Reinforcement Learning

1. Safe and Robust Reinforcement Learning

“Run-Time Task Composition with Safety Semantics”
“Iterative Regularized Policy Optimization with Imperfect Demonstrations”
“Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation”
“Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning”
“Constrained Reinforcement Learning Under Model Mismatch”
“EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data”

2. Exploration and Sample Efficiency

“Rich-Observation Reinforcement Learning with Continuous Latent Dynamics”
“How to Explore with Blindness: State Entropy Maximization in POMDPs”
“Compound Returns Reduce Variance in Reinforcement Learning”
“Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization”
“Single-Trajectory Distributionally Robust Reinforcement Learning”
“Scalable Safe Policy Improvement for Factored Multi-Agent MDPs”

3. Learning Representations and Models

“Learning Causal Dynamics Models in Object-Oriented Environments”
“Foundation Policies with Hilbert Representations”
“Simple Ingredients for Offline Reinforcement Learning”
“Learning the Target Network in Function Space”
“Skill-Enhanced Reinforcement Learning Acceleration from Demonstrations”

4. Multi-Agent Reinforcement Learning

“Subequivariant Reinforcement Learning in 3D Multi-Entity Physical Environments”
“FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning”
“Impact of Decentralized Learning on Player Utilities in Stackelberg Games”

5. Interactive and Online Learning

“Rapid Learning without Catastrophic Forgetting in the Morris Water Maze”
“Agnostic Interactive Imitation Learning: New Theory and Practical Algorithms”
“Learning with Adaptive Resource Allocation”

6. Imitation and Preference Learning

“Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning”
“Imitation Learning from Purified Demonstrations”
“Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint”
“Preference-Based Reinforcement Learning”

7. Deep Learning Approaches and Optimization

“ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages”
“Learning Causal Dynamics Models in Object-Oriented Environments”
“Multi-Agent Policy Learning with Evolutionary Strategies”
“Bayesian Design Principles for Offline-to-Online Reinforcement Learning”

8. Policy Optimization and Generalization

“Cross-Domain Policy Adaptation by Capturing Representation Mismatch”
“Cross-Domain Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices”
“How to Leverage Diverse Demonstrations in Offline Imitation Learning”

9. Reward Function Design and Optimization

“Augmenting Decision with Hypothesis in Reinforcement Learning”
“Reward Warping for Robust Offline Reinforcement Learning”

10. Miscellaneous Challenges in Reinforcement Learning

“Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems”
“Framework for Markov Decision Processes with Temporal Logic Specifications”

Cluster id: 3

1. Differential Games and Game Theory

“State-Constrained Zero-Sum Differential Games with One-Sided Information”

2. Compression and Data Efficiency

“Debiased Distribution Compression”

3. Complexity and Generalization in Optimization

“Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization”

4. Convergence and Gradient Methods

“On the Last-Iterate Convergence of Shuffling Gradient Methods”
“Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning”
“Convergence of Some Convex Message Passing Algorithms to a Fixed Point”

5. Reinforcement Learning and Bandit Problems

“Learning from Streaming Data when Users Choose”
“Best Arm Identification for Stochastic Rising Bandits”
“Leveraging (Biased) Information: Multi-armed Bandits with Offline Data”

6. Change Detection and Sequential Methods

“Reducing sequential change detection to sequential estimation”
“Sequential Kernel Goodness-of-fit Testing”

7. Bilevel and Hyperparameter Optimization

“Distributed Bilevel Optimization with Communication Compression”
“Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization”

8. Stochastic and Composite Optimization

“MoMo: Momentum Models for Adaptive Learning Rates”
“Optimal Kernel Quantile Learning with Random Features”
“Efficient Stochastic Approximation of Minimax Excess Risk Optimization”

9. Sparse and High-Dimensional Problems

“Weighted distance nearest neighbor condensing”
“Improving Computational Complexity in Statistical Models with Local Curvature Information”
“Sparse Dimensionality Reduction Revisited”

10. Matrix and Tensor Methods

“MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations”
“On the Error-Propagation of Inexact Hotelling’s Deflation for Principal Component Analysis”
“Random matrix theory improved Frechet mean of symmetric positive definite matrices”

11. Bayesian Methods and Inference

“Faster Sampling via Stochastic Gradient Proximal Sampler”
“Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms”
“Efficient Algorithms for Empirical Group Distributional Robust Optimization and Beyond”

12. Kernel Methods and Gaussian Processes

“Slicing with Stein Kernels for Faster Adaptive Monte Carlo”
“KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions”

13. Graph Clustering and Network Optimization

“Dynamic Metric Embedding into lp Space”
“Faster Streaming and Scalable Algorithms for Finding Directed Dense Subgraphs in Large Graphs”
“Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits”

14. Theoretical and Practical Improvements in Machine Learning

“Fair Resource Allocation in Multi-Task Learning”
“Faster Adaptive Optimizers for Nonconvex Problems: From Theory to Practice”
“Robust and Conjugate Gaussian Process Regression”

15. Quantum Algorithms and Applications

“Quantum Algorithms and Lower Bounds for Finite-Sum Optimization”

Cluster id: 4

Best Practices and Methods in Aligning LLMs with Human Preferences and Ensuring Ethical and Safe AI Development

1. Improving Language Models with Guidance and Optimization Techniques

“Stay on Topic with Classifier-Free Guidance”
“Improving Factuality and Reasoning Language Models through Multiagent Debate”
“Improving Open-Ended Text Generation via Adaptive Decoding”
“Towards Efficient Exact Optimization of Language Model Alignment”
“Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam Generation”
“Revisiting Character-level Adversarial Attacks for Language Models”
“Learning to Model the World With Language”
“Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution”
“Optimizing watermarks for large language models”

2. Aligning Language Models with Human Preferences and Values

“Position Paper: A Safe Harbor for AI Evaluation and Red Teaming”
“Human Alignment of Large Language Models through Online Preference Optimisation”
“Self-Rewarding Language Models”
“Nash Learning from Human Feedback”
“Position Paper: Building Guardrails for Large Language Models”
“RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content”
“Exploring the LLM Journey from Cognition to Expression with Linear Representations”

3. Safety, Reliability, and Robustness in Large Language Models

“PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition”
“Position Paper: Quantifying Policy Impacts on Online Harms – A Call for Machine Learning-powered Assessment of the EU Digital Services Act”
“Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications”
“Position Paper: Key Claims in LLM Research Have a Long Tail of Footnotes”
“AI for software development at Google”
“Transforming and Combining Rewards for Aligning Large Language Models”

4. Adaptive and In-Context Learning in Large Language Models

“In-Context Unlearning: Language Models as Few-Shot Unlearners”
“Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective”
“ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models”
“Learning and Forgetting Unsafe Examples in Large Language Models”
“Understanding the Learning Dynamics of Alignment with Human Feedback”
“Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision”
“Neighboring Perturbations of Knowledge Editing on Large Language Models”

5. Enhancements and Innovations in Model Functionality and Coding

“BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback”
”$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts”
“Physics of Language Models: Part 3.1, Knowledge Storage and Extraction”
“AutoOS: Make Your OS More Powerful by Exploiting Large Language Models”

6. Discovery and Research with Generative AI

“LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery”
“BetterV: Controlled Verilog Generation with Discriminative Guidance”
“MAX: Multimodal Extreme Networks for Dynamic Tasks”
“RLFV: Learning from Verbal Feedback without Overgeneralization”

7. Multimodal and Specialized AI Applications

“TravelPlanner: A Benchmark for Real-World Planning with Language Agents”
- Testing the limit of what current LLMs can do on the planning front. For now, limited. Provides a benchmark / sandbox to test LLMs planning capabilities.
”$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts”
“Keypoint-based Progressive Chain-of-Thought Distillation for LLMs”
“Using Left and Right Brains Together: Towards Vision and Language Planning”

8. Model Interpretability and Transparency

“Understanding Preference Fine-Tuning for Large Language Models”
“Self-Exploring Language Models: Active Preference Elicitation for Online Alignment”
“AI Control: Improving Safety Despite Intentional Subversion”
“Neighboring Perturbations of Knowledge Editing on Large Language Models”
“Feedback Loops With Language Models Drive In-Context Reward Hacking”

9. Task-Specific Performance and Capabilities of LLMs

“TravelPlanner: A Benchmark for Real-World Planning with Language Agents”
”$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts”
“Physics of Language Models: Part 3.1, Knowledge Storage and Extraction”
“Embodied CoT Distillation From LLM To Off-the-shelf Agents”
“Vision-Language Models Provide Promptable Representations for Reinforcement Learning”

Cluster id: 5

Dynamics and Optimization in Neural Network Training

1. Optimization and Training Dynamics

“Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks”
“SGD vs GD: Rank Deficiency in Linear Networks”
“Critical feature learning in deep neural networks”
“On the Implicit Bias of Adam”
“Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions”
“Where Do Large Learning Rates Lead Us? A Feature Learning Perspective”
“Gradient Descent with Polyak’s Momentum Finds Flatter Minima via Large Catapults”
“The optimization landscape of Spectral neural network”
“An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks”

2. Neural Architecture and Generalization

“Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks”
“Random matrix theory analysis of neural network weight matrices”
“Expressivity of Neural Networks with Fixed Weights and Learned Biases”
“Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning”
“Position Paper: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning”
“Deconstructing the Goldilocks Zone of Neural Network Initialization”
“On a Neural Implementation of Brenier’s Polar Factorization”
“Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape”

3. Regularization and Robustness

“SGD vs GD: Rank Deficiency in Linear Networks”
“On the Implicit Bias of Adam”
“Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks”
“On the Diminishing Returns of Width for Continual Learning”
“Can Implicit Bias Imply Adversarial Robustness?”
“Loss landscape geometry reveals stagewise development of transformers”
“Invariant Representations of Neural Networks via Differential Regularization”

4. Neural Network Implementation and Architecture Search

“Expressivity of Neural Networks with Fixed Weights and Learned Biases”
“Adaptive Gradient Regularization”
“Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks”
“Fully Asynchronous CNNs: New Architecture and Implementation”
“Proxy Constraints for Improved Neural Architecture Search”
“Data-free Neural Representation Compression with Riemannian Neural Dynamics”

5. Neuro-Symbolic AI and Neuroscience Inspired Networks

“Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit”
“Neural Symmetry Detection for Learning Neural Network Constraints”
“Progress Measures for Grokking on Real-world Tasks”
“Neural Tangent Kernels for Axis-Aligned Tree Ensembles”
“Synaptic Plasticity for Dynamic Link Weight Adjustment”

6. Spiking Neural Networks and Neuroscience Models

“Sparsest Models Elude Pruning: An Exposé of Pruning’s Current Capabilities”
“Decoupling the Interaction of Spiking Neurons for Improved Learning Dynamics”
“SNNs in High-Dimensional Systems: New Approaches and Empirical Studies”
“Adaptive Spike-Timing Dependent Plasticity”
“Neuromorphic Learning Algorithms: A Comprehensive Survey”

7. Physics-Informed Neural Networks and Scientific Applications

“Data-free Neural Representation Compression with Riemannian Neural Dynamics”
“New Insights into Neural Network Feature Space”
“Challenges in Training PINNs: A Loss Landscape Perspective”
“On the metastability of learning algorithms in physics-informed neural networks: A case study on Schrödinger operators”
“Physics-Informed Neural Representations: From Theory to Practice”

8. Uncertainty and Bayesian Methods

“Amortized Variational Deep Kernel Learning”
“Bayesian Adaptation of Network Depth and Width for Continual Learning”
“Beyond Implicit Bias: The Insignificance of SGD Noise in Online Learning”
“Neural Estimation of Mutual Information without Test-Time Optimization”

9. High-Dimensional Data and Representation Learning

“Unsupervised Feature Learning for High-Dimensional Data”
“Invariant Feature Learning with Nonlinear Projections”
“Deconstructing the Goldilocks Zone of Neural Network Initialization”
“Learning Representations and Associations with Neural Networks”
“Neural Compression Algorithms: A New Frontier in Data Processing”

Cluster id: 6

Advanced Graph-Based Machine Learning Techniques and Applications

1. Graph Neural Networks (GNN) and Enhancements

“Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning”
“Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing”
“EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction”
“On the Role of Edge Dependency in Graph Generative Models”
“Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction”
“SLOG: An Inductive Spectral Graph Neural Network Beyond Polynomial Filter”
“Finding Paths by Graph Neural Network in Homogeneous and Heterogeneous Graphs”
“Learning Graph Representation via Graph Entropy Maximization”
“PANDA: Expanded Width-Aware Message Passing Beyond Rewiring”
“Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs”
“Pairwise Alignment Improves Graph Domain Adaptation”

2. Graph Representation, Encoding, and Learning

“Geometric Algebra based encoding for graph prompting”
“Graph2Token: Make LLMs Understand Molecule Graphs”
“Quantum Positional Encodings for Graph Neural Networks”
“Generalized Sobolev Transport for Probability Measures on a Graph”
“Graph Positional and Structural Encoder”
“Relational Deep Learning: Graph Representation Learning on Relational Databases”
“Interactome-scale comparison of co-immunoprecipitation and yeast two-hybrid assays for protein interaction prediction”
“Geometric Algebra based encoding for graph prompting”
“Multi-View Stochastic Block Models”
“Graph Adversarial Diffusion Convolution”

3. Diffusion Models for Graphs

“Hyperbolic Geometric Latent Diffusion Model for Graph Generation”
“Graph Diffusion Models and Applications to Semi-Supervised Learning”

4. Knowledge Graphs, Causality, and Causal Learning

“Generalization Error of Graph Neural Networks in the Mean-field Regime”
“Causal Reasoning in Graphs: Aligning with Neural Tangent Kernels”
“Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency”
“Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization”

5. Applications in Biology and Chemistry

“Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks”
“A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction”
“Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry Prediction”

6. Graph Transformers

“Graph External Attention Enhanced Transformer”
“Aligning Transformers with Weisfeiler-Leman”
“Less is More: on the Over-Globalizing Problem in Graph Transformers”
“Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers”
“Position: Graph Foundation Models Are Already Here”
“Weisfeiler Leman for Euclidean Equivariant Machine Learning”
“Homomorphism Counts for Graph Neural Networks: All About That Basis”
“A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer”
“Diffusing Knowledge in Graph Transformers”

7. Explainability and Robustness

“Encoding to Explain: Enhancing Explainable Machine Learning for Graphs”
“How Interpretable Are Interpretable Graph Neural Networks?”
“Graph Neural Networks Use Graphs When They Shouldn’t”
“Graph Distillation with Eigenbasis Matching”
“Exploring Correlations of Self-Supervised Tasks for Graphs”
“Explaining Graph Neural Networks via Structure-aware Interaction Index”

8. Unsupervised and Self-supervised Learning

“Unsupervised Episode Generation for Graph Meta-learning”
“Disentangled Graph Self-supervised Learning under Distribution Shifts”
“From Coarse to Fine: Enable Comprehensive Graph Self-supervised Learning with Multi-granular Semantic Ensemble”

9. Graph Data Handling, Augmentation, and Mixup

“Graph Data Augmentation and Mixup Techniques for Improved Model Performance”
“Perfect Alignment May be Poisonous to Graph Contrastive Learning”
“Augmentation and Mixup for Enhancing Graph Learning Techniques”

10. Advanced Topics in Graph Theory and Learning

“Editors, Readers, Writers: Graph Neural Network-based Text Editors”
“Incremental Topological Ordering and Cycle Detection with Predictions”
“Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks”
“Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning”

Cluster id: 7

Advanced Generative Models and Methods in Machine Learning

1. Diffusion Models & Generative Methods

“Diffusion Language Models Are Versatile Protein Learners”
“Training-Free Inference Acceleration of Diffusion Models”
“Hidden Learning Dynamics of Capability before Behavior in Diffusion Models”
“Reflected Flow Matching”
“Diffusion Models Encode the Intrinsic Dimension of Data Manifolds”
“Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations”
“Efficient Mixture Learning in Black-Box Variational Inference”
“Enhancing Implicit Shape Generators Using Topological Regularizations”
“Particle Denoising Diffusion Sampler”
“Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases”
“Enhancing Trajectory Prediction through Self-Supervised Waypoint Distortion Prediction”
“Switched Flow Matching: Eliminating Singularities via Switching ODEs”
“Fast Timing-Conditioned Latent Audio Diffusion”
“Switched Flow Matching: Eliminating Singularities via Switching ODEs”

2. Protein Design and Simulation

“AlphaFold Meets Flow Matching for Generating Protein Ensembles”
“Floating Anchor Diffusion Model for Multi-motif Scaffolding”
“Sequence-Specific Folding by Stochastic Diffusions”
“Generative Diffusion Networks for RNA Design”
“Protein Conformation Generation via Diffusion Processes”
“Tutorial Design and Verification for Protein Folding Models”
“Diffusion-Based Methods for Protein Design”
“Topological Regularizations in Diffusion Models for Shape Generations”
“Leverage Denoising Diffusion Model for Training Stability”

3. Conditional Generative Models

“Dynamic Multi-Resolution Denoising Models for Inference”
“Retrieval-Augmented Diffusion for 3D Molecule Generation”
“Conditional Sampling with Discrete Diffusion Models”
“Model-Free Adaptive Control with Denoising Diffusion”
“From Fourier to Neural ODEs: Flow Matching for Modeling Complex Systems”

4. Image and Video Generation & Editing

“MagicPose: Realistic Human Pose and Facial Expression Retargeting with Identity-aware Diffusion”
“Single-Model Attribution of Generative Models Through Final-Layer Inversion”
“Compositional Image Decomposition with Diffusion Models”
“Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations”
“Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions”
“Gaussian Pro: 3D Gaussian Splatting with Progressive Propagation”

5. Structure-based Drug and Molecular Design

“Drug Discovery with Dynamic Goal-aware Fragments”
“Molecular Design Optimization Using Energy-Based Models”
“Structure-based drug design by denoising voxel grids”
“Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Enhanced Goal Directed Generation”
“Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design”

6. Scientific Applications & Physical Systems Modeling

“OxyGenerator: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning”
“Physical Systems Simulation Using Deep Learning”
“Neural Jump-Diffusion Temporal Point Processes”
“Robotics and Dynamics Systems”
“Data-efficient Algorithms for Scientific Simulation”

7. Machine Learning for Optimization & Inference

“Robust Classification via a Single Diffusion Model”
“Improved Variational Inference via Denoising Diffusion”
“Mean-field Underdamped Langevin Dynamics and its Spacetime Discretization”
“Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models”
“Adaptive Sampling of k-Space in Magnetic Resonance for Fast Pathology Prediction”

8. Audio and Music Generation

“Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion”
“Time Series Diffusion in the Frequency Domain”
“Neural Diffusion Models for Long-form Music Generation”
“Latent Audio Diffusion Models”
“MusicFlow: Cascaded Flow Matching for Text Guided Music Generation”

Cluster id: 8

Efficient Training and Inference for Large Language Models

1. Efficient and Scalable LLM Inference and Training

“Accelerated Speculative Sampling Based on Tree Monte Carlo”
“CaM: Cache Merging for Memory-efficient LLMs Inference”
“DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving”
“Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference”
“Fewer Truncations Improve Language Modeling”
“GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection”
“Getting the most out of your tokenizer for pre-training and domain adaptation”
“HexGen: Generative Inference of Large Language Model over Heterogeneous Environment”
“Learning Linear Block Error Correction Codes”
“Scaling Beyond the GPU Memory Limit for Large Mixture-of-Experts Model Training”
“Trainable Transformer in Transformer”
“Tandem Transformers for Inference Efficient LLMs”
“Transformers with Loss Shaping Constraints for Long-Term Time Series Forecasting”
“Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models”
“Wukong: Towards a Scaling Law for Large-Scale Recommendation”

2. Transformer Architectures and Enhancements

“Better & Faster Large Language Models via Multi-token Prediction”
“Bifurcated Attention for Single-Context Large-Batch Sampling”
“CLLMs: Consistency Large Language Models”
“Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks”
“From Generalization Analysis to Optimization Designs for State Space Models”
“Improving Transformers with Dynamically Composable Multi-Head Attention”
“LeaPformer: Enabling Linear Transformers for Autoregressive and Simultaneous Tasks via Learned Proportions”
“On the Embedding Collapse when Scaling up Recommendation Models”
“Repeat After Me: Transformers are Better than State Space Models at Copying”
“SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized BatchNorm”
“State-Free Inference of State-Space Models: The Transfer Function Approach”

3. Quantization and Compression Techniques

“Accurate LoRA-Finetuning Quantization of LLMs via Information Retention”
“BiLLM: Pushing the Limit of Post-Training Quantization for LLMs”
“Extreme Compression of Large Language Models via Additive Quantization”
“Inference in Memory: Co-locating INference and Far-memory Efficiently”
“LQER: Low-Rank Quantization Error Reconstruction for LLMs”
“Outlier-aware Slicing for Post-Training Quantization in Vision Transformer”
“QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks”
“Rethinking Optimization and Architecture for Tiny Language Models”
“SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention”

4. Parameter-Efficient Fine-Tuning and Adaptation

“DoRA: Weight-Decomposed Low-Rank Adaptation”
“Efficient World Models with Time-Aware and Context-Augmented Tokenization”
“Flextron: Many-in-One Flexible Large Language Model”
“LoRA+: Efficient Low Rank Adaptation of Large Models”
“Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation”
“PEFT: Parameter-Efficient Fine-Tuning with Increased Robustness”
“Pruning Small Pre-Trained Weights $\textit{Irreversibly}$ and $\textit{Monotonically}$ Impairs “Difficult” Downstream Tasks in LLMs”
“SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters”

5. Algorithms for Language and Sequence Tasks

“A Fast Tree Search Procedure for Language Models”
“Algorithm and Hardness for Dynamic Attention Maintenance in Large Language Models”
“Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT”
“Cell2Sentence: Teaching Large Language Models the Language of Biology”
“Improving Token-Based World Models with Parallel Observation Prediction”
“Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment Problem”
“Memory Efficient Neural Processes via Constant Memory Attention Block”
“NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models”
“Sparse is Enough in Fine-tuning Pre-trained Large Language Models”

6. Novel Training and Learning Paradigms

“Adapted Training of Large-Scale Non-parallel Translation Models for e-LTE License Test Extraction”
“Amortizing Pragmatic Program Synthesis with Rankings”
“Bayesian Program Learning by Decompiling Amortized Knowledge”
“Bidirectional Equivariant Long-Range DNA Sequence Modeling”
“DeepCortex: Deep Learning Training on Low-Memory Devices through Artificial Intelligence”
“Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch”
“Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs”
“Practical Performance Guarantees for Pipelined DNN Inference”
“Pre-Training Protein Bi-level Representation Through Span Mask Strategy On 3D Protein Chains”

7. Efficient Computation Techniques in Various Domains

“Auto-Regressive Next-Token Predictors are Universal Learners”
“Auctionformer: A Unified Deep Learning Algorithm for Solving Equilibrium Strategies in Auction Games”
“CARTE: Pretraining and Transfer for Tabular Learning”
“Differentiable Model Scaling using Differentiable Topk”
“Efficient and Effective Time-Series Forecasting with Spiking Neural Networks”
“ELF: Encoding Speaker-Specific Latent Speech Features for Speech Synthesis”
“Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling”
“How Do Nonlinear Transformers Learn and Generalize in In-Context Learning?”
“PIDformer: Transformer Meets Control Theory”
“Sparse-IFT: Sparse Iso-FLOP Transformations for Maximizing Training Efficiency”

Cluster id: 9

Advanced Techniques and Theoretical Insights in Machine Learning

1. Data Augmentation and Domain Adaptation

“The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective”
“Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data”
“Adaptive Robust Learning using Latent Bernoulli Variables”
“Scaling Laws for the Value of Individual Data Points in Machine Learning”

2. Causal Inference and Treatment Effects

“Can a Few Decide for Many? The Metric Distortion of Sortition”
“Individual Treatment Effect Estimation with Confounding Adjustment”
“Adaptive Learning in Personalized Treatments and Policies”
“Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments”

3. Fairness and Ethics in Machine Learning

“Fair Off-Policy Learning from Observational Data”
“Attribution-based Explanations that Provide Recourse Cannot be Robust”
“The Relative Value of Prediction in Algorithmic Decision Making”
“Fair Data Representation for Machine Learning at the Pareto Frontier”

4. Adversarial Examples and Robustness

“Adversarially Robust Deep Multi-View Clustering”
“Robust Universal Adversarial Perturbations”
“Efficient Online Set-valued Classification with Bandit Feedback”
“Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection”

5. Explainability and Interpretability

“TimeX++: Learning Time-Series Explanations with Information Bottleneck”
“Explaining Temporal Black-Box Models via Functional Decomposition”
“Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution”
“Counterfactual Explanations for Structured Prediction”

6. Machine Learning Theory

“Provable Benefit of Cutout and CutMix for Feature Learning”
“Interplay of ROC and Precision-Recall AUCs: Theoretical Limits and Practical Implications”
“Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability”
“On the sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery”

7. Time-Series and Sequential Data

“Dynamic Survival Analysis with Controlled Latent States”
“Generalization Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability”
“Meta-Learners for Partially-Experimental Treatment Effect Estimation”
“Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning”

8. Optimization and Learning Algorithms

“Conformal Prediction for Multi-dimensional Time Series by Ellipsoidal Sets”
“Kernel Debiased Plug-in Estimation”
“Adaptive Constraint Modification via Grouping and Selection for Hardness-Preserving MILP Instance Generation”
“Optimal Kernel Choice for Score Function-based Causal Discovery”

9. Uncertainty Quantification and Calibration

“Uncertainty Estimation by Density Aware Evidential Deep Learning”
“Robust Universal Adversarial Perturbations”
“T-Cal: An Optimal Test for the Calibration of Predictive Models”
“Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Unsupervised Domain Adaptation”

10. Clustering and Anomaly Detection

“ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models”
“Scaling Laws for the Value of Individual Data Points in Machine Learning”
“Active Statistical Inference”
“Robust Universal Adversarial Perturbations”

11. Data Selection and Labeling

“Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints”
“Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer”
“Careful with that Scalpel: Improving Gradient Surgery with an EMA”
“Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning”

12. Deep Learning Methods

“Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization”
“Robust Universal Adversarial Perturbations”
“Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments”
“Generalized Feature Attribution for Large Models & Data”

13. Bayesian Learning

“Bayesian Structural Causal Models for High-dimensional Causal Inference”
“Think Big: Estimating the Long-Term Effects of Large-Scale Policies with Bayesian Models”
“Bayesian Uncertainty for Gradient Aggregation in Multi-task Learning”
“Bayesian Neural Network Adaptation with Integrative Covariance Estimation”

14. Image and Vision

“Sensitivity Sampling in Pixel-wise Regression Learning”
“Pivot: Architecture-Specific Adaptations in Image-based Tasks”
“Trained Random Forests Completely Reveal your Dataset”
“Efficient Precision and Recall Metrics for Assessing Generative Models using Hubness-aware Sampling”

15. Semi-Supervised and Unsupervised Learning

“SSL4Q: Semi-Supervised Learning of Quantum Data with Application to Quantum State Classification”
“Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choice”
“Learning Variable Latent Layers in Semi-supervised Neural Networks”
“Non-linear Gaussian Processes for Unsupervised Generalizations”

Basic DSPy RAG tutorial on DataGrapple blog posts

Tue, 16 Apr 2024 00:00:01 +0000

Basic DSPy RAG tutorial on DataGrapple blog posts

Disclaimer: The experiments detailed below are conducted independently and are not related to my current employment. They represent personal endeavors undertaken during my free time, primarily over weekends, with the aim of staying informed about the latest technological developments in the field of large language models. Nothing contained within this blog post should be construed as financial advice or an encouragement to engage in investment activities. The insights derived from the large language models (LLMs) are merely restatements of publicly available information sourced from DataGrapple blogs. These blogs primarily focus on describing the current state of the credit default swap (CDS) market and do not endeavor to predict future market movements.

This blog is more a note to self for experimenting further with DSPy (arXiv, GitHub) than a pedagogical or original intro to the framework. It essentially follows this weaviate tutorial with small adaptations, notably removing the weaviate part of it, and replacing their retrieval module by a very basic local search in the embeddings. I typically experiment against the jargon-heavy DataGrapple blog posts written by French portfolio managers. It serves as a good ‘stress test’ for general-purpose NLP tools.

tl;dr The DSPy framework helps optimizing the prompts to obtain a better and more focused RAG.

We go automatically from the generic:

Answer questions based on the context.

---

Follow the following format.

Context: may contain relevant facts
Question: ${question}
Reasoning: Let's think step by step in order to ${produce the answer}. We ...
Answer: ${answer}

---

to:

Based on the provided business and financial contexts, provide detailed and specific answers that fully address the posed questions, drawing explicitly from the information given.

---

Follow the following format.

Context: may contain relevant facts
Question: ${question}
Reasoning: Let's think step by step in order to ${produce the answer}. We ...
Answer: ${answer}

---

which is a prompt tailored to our specific corpus.

DSPy RAG tutorial

First, install DSPy.

!pip install dspy-ai

Download a corpus of blogs against which we can look for precise answers.

!wget https://sp500-histo.s3.ap-southeast-1.amazonaws.com/blogs

import os
import pickle
import numpy as np
import pandas as pd
import dspy
from openai import OpenAI
from sklearn.metrics.pairwise import cosine_similarity
from tqdm import tqdm


os.environ['OPENAI_API_KEY'] = YOUR_OWN_OPENAI_API_KEY


client = OpenAI()

with open('blogs', 'rb') as f:
    blogs = pickle.load(f)

len(blogs)

blogs[0]

{'title': 'That Is A Big Deal',
 'author': 'jbchevrel',
 'date': '2019-04-12',
 'link': 'https://www.datagrapple.com/Blog/Show/12272/that-is-a-big-deal.html',
 'content': 'In a decently risk-on session (CDX IG -2.8 CDX HY -8.9 SPX @ 2,900), the CDS of Anadarko Petroleum Corp. (APC) outperformed the broader market, tightening by c65bp. Bonds are also 75-100bp tighter. That is because the oil giant Chevron Corp. (CVX) agreed to buy APC. The equity is valued $33B, which will be paid in stocks and cash (75/25: 0.3869 CVX shares and $16.25 in cash per APC share). That is a 39% premium therefore APC share soared towards the offer price (+23% on day). The transaction is expected to close in 2H19. CVX management doesn’t expect any regulatory issues. From a credit standpoint, CVX will assume $15B net debt from APC, making APC EV c$50B. CVX will issue 200M shares and pay $8B in cash. A very tight name, CVX widened 6bp to 33bp mid, making the APC/CVX spread tighten 71bp, from +70bp to -1bp! CVX is not really a story for credit. Indeed, CVX has c$9.4B cash on hand and past experience proves that it generates $8B+ FCF per year at $50-55/bbl crude (vs now WTI $64), so it looks unlikely that they will fund the non-share cash part (c$8B) with debt. And even in the unlikely event it would do that, the combined leverage would be somewhere around 1x. Adding to this point, the news that 1/ CVX expects to realize $2B synergies (proceeds partly used for debt reduction) 2/ CVX plans to sell $15-20bn of assets in 2020-2022 confirms that CVX credit is not in trouble anytime soon. Therefore the consensus expects CVX to keep its current rating (AA/Aa2), while APC will converge to CVX from its Ba1/BBB, although we don’t know if CVX will explicitly guarantee them. CVX aside, this news dragged all the US/Canada IG energy tighter, with Hess -22 Devon -15 Encana -13, partly because the market knew APC was a target and consolidation was expected. This acquisition shows the importance of size in this business, where the biggest and the most diversified players do well. '}

We can embed the blogs using the text-embedding-3-small model.

# client = OpenAI()

# embeddings = []
# for blog in tqdm(blogs):
#   content = blog["content"]
#   emb = client.embeddings.create(input=content, model="text-embedding-3-small").data[0].embedding

#   embeddings.append([
#       blog["title"],
#       blog["author"],
#       blog["date"],
#       blog["link"],
#       content,
#       emb])

#   df_embeddings = pd.DataFrame(
#     embeddings,
#     columns=["title", "author", "date", "link", "content", "embedding"]
#   )

# df_embeddings.to_parquet("dg_blogs_with_openai_text-embedding-3-small_embeddings.parquet")

No need to recompute the embeddings again and again. Let’s fetch them from s3.

!wget https://sp500-histo.s3.ap-southeast-1.amazonaws.com/dg_blogs_with_openai_text-embedding-3-small_embeddings.parquet

df_embeddings = pd.read_parquet("dg_blogs_with_openai_text-embedding-3-small_embeddings.parquet")

df_embeddings

	title	author	date	link	content	embedding
0	That Is A Big Deal	jbchevrel	2019-04-12	https://www.datagrapple.com/Blog/Show/12272/th...	In a decently risk-on session (CDX IG -2.8 CDX...	[0.023862849920988083, -0.02890099585056305, 0...
1	Only Game In Town	jbchevrel	2019-04-11	https://www.datagrapple.com/Blog/Show/12271/on...	Today, the ECB pretty much dictated the price ...	[-0.0019143268000334501, 0.014265304431319237,...
2	Impairment Bites	jbchevrel	2019-04-10	https://www.datagrapple.com/Blog/Show/12270/im...	HEMA (short for 4 unpronounceable Dutch words)...	[-0.012396507896482944, 0.0550399050116539, 0....
3	On The Red	jbchevrel	2019-04-09	https://www.datagrapple.com/Blog/Show/12269/on...	Today the 5y CDS of Crown Resorts Ltd (CWNAU) ...	[-0.031644079834222794, 0.004807306919246912, ...
4	Shipping Names Rocked	jbchevrel	2019-04-08	https://www.datagrapple.com/Blog/Show/12268/sh...	Today CMA CGM (CMACG) and Hapag-Lloyd AG (HPLG...	[0.01699497364461422, 0.003022479824721813, 0....
...	...	...	...	...	...	...
1233	PostNL (TNTNA) Delivers Good News	HCM	2013-12-05	https://www.datagrapple.com/Blog/Show/10/postn...	This is the first of our daily comment, a colu...	[0.0008119812700897455, 0.026372777298092842, ...
1234	High Trading Activity at the End of 2013	HCM	2013-12-04	https://www.datagrapple.com/Blog/Show/2/high-t...	\r\nThe brown area chart tracks the evolution ...	[0.020368456840515137, 0.0014192602830007672, ...
1235	European Banks 2013: UCGIM vs STANLN	HCM	2013-12-04	https://www.datagrapple.com/Blog/Show/3/europe...	Grapple selection lets the user browse with a...	[-0.020505966618657112, 0.008376846089959145, ...
1236	Heinz (HNZ) – Too Hot for IG	HCM	2013-12-04	https://www.datagrapple.com/Blog/Show/5/heinz-...	This view gives a representation of the intens...	[-0.004270065575838089, -0.009213356301188469,...
1237	Monthly CDS Report	HCM	2013-11-29	https://www.datagrapple.com/Blog/Show/1/monthl...	Over the last month, the European CDS market h...	[-0.00585611816495657, 0.008636543527245522, 0...

1238 rows × 6 columns

A bit useless, but suggested by Google Colab AI:

# generated by google colab ai
from matplotlib import pyplot as plt
import seaborn as sns
df_embeddings.groupby('author').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))
plt.gca().spines[['top', 'right',]].set_visible(False)

embedding_matrix = np.stack(df_embeddings['embedding'].apply(np.array))

embedding_matrix.shape

(1238, 1536)

Basic retriever ‘module’

def embed_text(text):
    response = client.embeddings.create(
        input=text,
        model="text-embedding-3-small")
    return response.data[0].embedding

# the most naive and simple retriever
def retrieve(query, k=5):
    query_embedding = embed_text(query)
    scores = cosine_similarity(np.array(query_embedding).reshape(1, -1), embedding_matrix)
    top_idx = scores.argsort()[0][::-1][:k]
    return df_embeddings.iloc[top_idx]['content'].values.tolist()

retrieved_blogs = retrieve("What business did Conagra Brands Inc. spin off on November 9, 2016?")

retrieved_blogs

['CAG ( Conagra Brands Inc. )  was a 12BUSD revenues food US company based in Chicago. On November 9, 2016, the company completed the spin-off of Lamb Weston - essentially its frozen potato fries  business (a 4BUSD revenue business). French fries and any sort of fried potatoes is a sound investment. Half of the debt of Conagra was pushed to the new company LW (( Lamb Weston Holdings Inc. ). From a BBB- company, the new Conagra is now BBB while  Lamb Weston is a BB. According to last Friday’s ISDA determination committee, a CDS holder having 1MUSD protection on CAG is now having 500k on new CAG and 500k on LW.  New CAG is indicated 20bps tighter while LW is 40bps wider. CAG is also a member of the investment grade credit indices in the US, the CDX.IG, in all series up to the latest the series 27. All credit indices members will also be split and will have 126 reference entities, CAG and LW being half weighted. This credit event is a source of large operations for credit derivatives trade processing in the next days.\n\nMeanwhile, the broader credit market went through a slow session with European investment grade risk being the weakest part of the investment spectrum.\n',
 'Overnight 3G Capital Partners and Berkshire Hathaway have offered to merge Kraft Foods Group with HNZ (HJ Heinz Company) to create The Kraft Heinz Co. 3G and Berkshire will own 51% on the new company, while existing Kraft shareholders will get 49% and a $16.5 special dividend. Importantly for the CDS market, no new debt will be issued and the management announced that they are committed to maintaining an investment grade rating going forward. The operation is effectively removing the LBO risk which was overhanging on the food and beverage sector as 3G now appears committed (at least in the medium term). So entire complex traded better today, but the most spectacular move was seen in HNZ’s 5 year CDS which gapped 119bps to 64bps as investors expect it to be a dead box going forward.',
 'Back in March, METFNL ( Metro AG ) announced that it would proceed with a demerger, in order to separate its food business (Food Co) from  its Consumer Electronics business (CE Co). METFNL held a conference call on the 6th September to give further details regarding the operation. All the financial liabilities of the group including bonds will be assumed by Food Co. Pension liabilities will be allocated 40% to Food Co and 60% to CE Co. Lease obligations will be 60% and 40%, and cash balances 75% and 25% respectively. Even though METFNL’s management expects both companies to maintain an investment grade rating after the spin-off, there won’t be any capital increase. That means that Food Co, due to the high debt load, will have at best weak credit metrics for its rating category and will be left with very limited financial leeway. Moody’s is understood to effectively ask the management to do more if Food Co is to be eligible for IG rating. That is probably why METFNL which will reference Food Co going forward has underperformed its peers during the last week. \nMeanwhile, the broader credit market spent another day looking at US interest rates. It dithered all session, and was unable to decide whether to go wider or tighter. Credit indices traded in a range (328/335 for iTraxx Crossover and 69/72 for iTraxx Main) and eventually closed bang in the middle of it.\n',
 'The failings of the food industry are in the spotlight again. This morning, ITV News and the Guardian have published a report that claims to have uncovered a series of safety breaches at the poultry plants of 2 Sisters Food Group (2SFG). The allegations involve tampering slaughter date of poultry being processed at sites that supply retailers including Tesco, Sainsbury’s, Marks & Spencer, Aldi and Lidl. The latters all said they would launch enquiries. Several of them decided to remove from their shelves products coming from the incriminated sites without further ado and have vowed to suspend their relationship with the firm until the investigation has concluded. 2SFG was founded in 1993 and now produces one third of all the poultry products consumed in the UK, and had revenues of £3.1Bln in 2016. Investors took the matter very seriously and parent company BOPRLN (Boparan Finance Plc) was under severe pressure as soon as the reports came out. Its 5-year risk premium jumped 169bps to 738bps. That is its widest level since it joined iTraxx Crossover 3 years ago.',
 'In an environment which has been supportive for credit over the last 12 months, a few names have been trading wider, especially in the Consumer Non-Cyclical sector. Releveraging seems to be on the agenda of a few companies through different channels. For instance, on the one hand, DGX ( Quest Diagnostics Inc) experienced weak trading conditions and decided to support its share price through share buy-backs. On the other hand, Campbell Soup (Campbell Soup Company) has been the subject of takeover speculations. Out of the money options on the stock experienced unusually high volumes recently and 5 year protection has been pushed aggressively wider. LBO could be back in people’s mind if they think systemic risks are receding.']

The top blog post from the retriever is indeed the one we need to answer the question:

retrieved_blogs[0]

'CAG ( Conagra Brands Inc. )  was a 12BUSD revenues food US company based in Chicago. On November 9, 2016, the company completed the spin-off of Lamb Weston - essentially its frozen potato fries  business (a 4BUSD revenue business). French fries and any sort of fried potatoes is a sound investment. Half of the debt of Conagra was pushed to the new company LW (( Lamb Weston Holdings Inc. ). From a BBB- company, the new Conagra is now BBB while  Lamb Weston is a BB. According to last Friday’s ISDA determination committee, a CDS holder having 1MUSD protection on CAG is now having 500k on new CAG and 500k on LW.  New CAG is indicated 20bps tighter while LW is 40bps wider. CAG is also a member of the investment grade credit indices in the US, the CDX.IG, in all series up to the latest the series 27. All credit indices members will also be split and will have 126 reference entities, CAG and LW being half weighted. This credit event is a source of large operations for credit derivatives trade processing in the next days.\n\nMeanwhile, the broader credit market went through a slow session with European investment grade risk being the weakest part of the investment spectrum.\n'

Definition of training, validation, and testing sets

From these blogs, we can extract (questions, answers), manually or with the help of a LLM.

Note that we won’t use the answers listed here to supervise the system, or evaluate the RAG answers. They are just there to satisfy curiosity.

questions_and_answers = [
    {"question": "What business did Conagra Brands Inc. spin off on November 9, 2016?",
     "answer": "Conagra completed the spin-off of Lamb Weston, which is primarily focused on the frozen potato fries business."},
    {"question": "How much of Conagra's debt was transferred to Lamb Weston after the spin-off?",
     "answer": "Half of Conagra’s debt was transferred to Lamb Weston."},
    {"question": "What was the revenue size of the spun-off business, Lamb Weston?",
     "answer": "Lamb Weston had revenues of $4 billion USD."},
    {"question": "What were the credit ratings for Conagra and Lamb Weston after the spin-off?",
     "answer": "After the spin-off, Conagra's credit rating was upgraded to BBB, and Lamb Weston's rating was BB."},
    {"question": "According to the ISDA determination committee, how was the CDS protection split between Conagra and Lamb Weston?",
     "answer": "A CDS holder who had $1 million USD protection on Conagra now has $500,000 on the new Conagra and $500,000 on Lamb Weston."},
    {"question": "What false information was circulated about VINCI?",
     "answer": "The false information claimed that VINCI had fired its finance chief amid irregularities and had discovered accounting errors."},
    {"question": "What was the market reaction when the false news about VINCI was released?",
     "answer": "The stock of VINCI plunged 18%, wiping more than €6 billion off its market capitalisation."},
    {"question": "How did VINCI's stock recover after the false report?",
     "answer": "The stock recovered most of its loss after the report was confirmed to be false."},
    {"question": "What was the impact on VINCI's 5-year risk premium following the incident?",
     "answer": "VINCI’s 5-year risk premium remained stable all day long, a couple of basis points tighter than the previous day's close at 58 bps."},
    {"question": "What did QUIBB's management confirm during their call with analysts?",
     "answer": "During their call with analysts, QUIBB's management stuck to their original plan regarding the rollout of the Burger King restaurants for 2017."},
    {"question": "What details did Banca Monte dei Paschi provide about their debt for equity exchange?",
     "answer": "anca Monte dei Paschi announced that they plan to offer equity worth between 85% and 100% of face value to the holders of their €4.3 billion outstanding subordinated bonds."},
    {"question": "What is the total capital increase planned by Banca Monte dei Paschi before the end of the year?",
     "answer": "Banca Monte dei Paschi has planned a total capital increase of €5 billion before the end of the year."},
    {"question": "What is the intended purpose of the capital increase by Banca Monte dei Paschi?",
     "answer": "The capital increase is instrumental in the process that should lead to the deconsolidation of their almost €28 billion portfolio of non-performing loans."},
    {"question": "How did the market react to the confidence in the success of Banca Monte dei Paschi's deal?",
     "answer": "Renewed confidence in the deal led investors to tighten MONTE’s 5-year risk premium by 12 bps to 392 bps, benefiting the whole Italian complex which outperformed other members of the European financial sector."},
    {"question": "What specific event impacted the credit of Intrum Justitia and its competitor Lindorff AB?",
     "answer": "ntrum Justitia, a Swedish debt collecting company, announced the acquisition of Lindorff AB, one of their Norwegian competitors. As a result, Lindorff’s debt will be refinanced, and CDS referencing LINDOR (Lock Lower Holding AS) will likely become orphaned."},
    {"question": "What factors are crucial in the business of car rental according to the blog?",
     "answer": "In the car rental business, maintaining a high utilization rate of the fleet and managing the sale of cars no longer wanted for customer use are crucial. Depreciation is highlighted as the highest cost in the industry."},
    {"question": "What unexpected information did Hertz report about their third-quarter earnings?",
     "answer": "Hertz reported third-quarter profits that badly trailed analysts’ estimates, attributing the shortfall to a decline in revenue and a drop in the values of its cars, which was unexpected and shocking to investors."},
    {"question": "What are Hertz’s new forecasts for Ebitda, and what could be the potential impact on its leverage?",
     "answer": "Hertz now forecasts an Ebitda of $575 million to $625 million for the year, which may push its leverage to more than 5 times by the end of the year. Moody’s indicated that such a leverage level could pressure Hertz's rating."},
    {"question": "What is noted about the investment opportunities in distress credits?",
     "answer": "Distress credits can offer a lot of convexity to investors, providing positive convexity for once, which suggests significant potential for gains as conditions improve."},
    {"question": "What external factors have influenced the perception of AK Steel's stock?",
     "answer": "The increase in iron ore prices by more than 50% this year, along with Donald Trump and Hillary Clinton's critical stance on China’s trade practices, have led equity investors to view AK Steel's stock as a call on the US steel recovery."},
    {"question": "What financial move did AK Steel Holding Corporation recently make regarding its shares?",
     "answer": "AK Steel Holding Corporation is issuing 52 million shares, which amounts to 25% of its market capitalization, to repay its outstanding borrowing under its asset-based revolver facility."},
    {"question": "What decision did the ISDA Determination Committee make regarding NSINO's recent debt management action?",
     "answer": "The ISDA Determination Committee decided on Friday, 22nd April, that NSINO’s action of extending bond maturities amounted to a restructuring credit event."},
    {"question": "What options do investors who have bought protection on NSINO's CDS have following the ISDA's decision?",
     "answer": "Investors who have bought protection on NSINO will have the opportunity to trigger their CDS contracts until the auction is held, though they are not obliged to do so."},
    {"question": "What changes occurred to NSINO's position in credit indices following the ISDA's decision?",
     "answer": "Following the ISDA's decision, NSINO has been spun off from all the credit indices to which it previously belonged."},
    {"question": "How has the restructuring event affected an investor with a position in iTraxx Crossover Series 23 involving NSINO?",
     "answer": "An investor (referred to as Investor X) who had a €74 million position in iTraxx Crossover Series 23 on Friday now has a €73 million position in the same series that no longer includes NSINO, along with a separate €1 million position on NSINO."},
    {"question": "What sectors does NXP Semiconductors NV primarily supply chips for?",
     "answer": "NXP Semiconductors is a leading chip supplier for smartphones and the auto industry, particularly after its acquisition of Freescale Semiconductor."},
    {"question": "What are some of the applications of NXP's chips in the auto industry?",
     "answer": "NXP's chips are used in a range of applications within the auto industry, including advanced driver assistance systems, infotainment, and in-vehicle networking between different car systems."},
    {"question": "How did NXP's Q1 2016 earnings report compare to analysts' expectations?",
     "answer": "NXP's Q1 2016 earnings report was in line with analysts' expectations, which surprised investors given the context."},
    {"question": "What did NXP's management say about the demand and market conditions during their earnings report?",
     "answer": "NXP's management mentioned that while overall demand continues to be subdued, the headwinds experienced last year should begin to subside in the coming quarter."},
    {"question": "What was NXP's revenue and gross margin outlook for Q2, and how did it compare to the consensus estimates?",
     "answer": "For Q2, NXP projected revenues of $2.3 to $2.4 billion and a gross margin of 49.5% to 50.5%, which was slightly ahead of the consensus estimates of $2.3 billion in revenue and a 49.8% gross margin."},
    {"question": "What steps has Ball taken to win regulatory approval for its acquisition of Rexam?",
     "answer": "To win regulatory approval, Ball announced that it had agreed with Rexam to sell plants in Brazil and Europe, along with innovation and support functions in Brazil, Britain, Germany, Switzerland, and the United States."},
    {"question": "Who is acquiring the assets divested by Ball and Rexam, and what is the significance of this acquisition?",
     "answer": "Ardagh Packaging (ARGID) is acquiring the divested assets, which is significant as it allows ARGID to acquire assets with combined revenues of around $3 billion."},
    {"question": "Why did ARGID's risk premium not participate in the recent rally of the iTraxx Crossover index?",
     "answer": "ARGID's risk premium did not rally with the iTraxx Crossover due to rumors that it was the leading bidder for the Ball-Rexam assets, which likely concerned investors about potential increased leverage."},
    {"question": "How did the market react to the announcement regarding ARGID's involvement in the Ball-Rexam deal?",
     "answer": "On the day of the announcement, investors marked ARGID’s 5-year CDS 36 basis points wider to 452 bps, reflecting concerns over the increased leverage from the acquisition."},
    {"question": "What adjustments did Alcoa Inc make to its global aluminum demand forecast for 2016?",
     "answer": "Alcoa Inc lowered its forecast for global aluminum demand in 2016, expecting a 5% increase, down from a previously projected 6% increase."},
    {"question": "How did Alcoa Inc revise its market deficit projection for 2016?",
     "answer": "Alcoa Inc decreased its market deficit projection for 2016 to a deficit of 1.1 million metric tons from 1.2 million metric tons estimated three months ago."},
    {"question": "What significant financial move did ArcelorMittal SA announce?",
     "answer": "ArcelorMittal SA announced they will buy back bonds after raising $3 billion earlier in the year through a rights issue. They plan to repurchase €1 billion of notes maturing in November 2017, €500 million due in March 2018, and $1.5 billion of securities maturing in June 2018."},
    {"question": "At what levels did ArcelorMittal SA propose to buy back their bonds, and how was this received?",
     "answer": "ArcelorMittal SA proposed to buy back their bonds at levels substantially above those at which they were trading in the secondary market, which puzzled some observers considering the already tight risk premia of the company."},
    {"question": "How did investors react to ArcelorMittal's bond buyback announcement?",
     "answer": "Investors reacted positively to the bond buyback announcement, sending the 5-year CDS of ArcelorMittal to its tightest level (584 bps) since the March roll."},
    {"question": "Who owns and manages OTE (Hellenic Telecommunications Organisation SA)?",
     "answer": "OTE is 40% owned and managed by Deutsche Telekom AG (DT) from Germany."},
     {"question": "What event influenced investor sentiment positively despite OTE's earnings?",
     "answer": "Investor sentiment was positively influenced by the Eurogroup meeting, where debt relief options for Greece were discussed earlier than expected."},
    {"question": "How did the positive developments at the Eurogroup meeting affect OTE’s financial market indicators?",
     "answer": "The positive tone at the Eurogroup meeting led to OTE’s 5-year risk premium tightening by 35 basis points to 431 bps."},
    {"question": "What allegations are connected to the investigation surrounding Ohio House Bill 6?",
     "answer": "The investigation involves accusations of bribery related to the passage of Ohio House Bill 6, with suggestions that illicit payments could have been as high as $60 million."},
    {"question": "What were the consequences for FirstEnergy's leadership following the federal corruption scandal?",
     "answer": "Following a board review triggered by the federal corruption scandal, FirstEnergy fired CEO Jones and other senior executives for violating the company’s policies and its code of conduct. Steven Strah was appointed as the acting CEO."},
    {"question": "What financial operations were highlighted for FirstEnergy in 2019?",
     "answer": "In 2019, FirstEnergy increased its cash reserves by approximately 60% or $250 million. It operationally generated over $2.47 billion, generated an additional $656 million from financing, and invested about $2.87 billion."},
    {"question": "How did the market react to Novafives' third-quarter performance and outlook?",
     "answer": "The company’s bonds were heavily punished, indicated approximately 6 points lower on the day, on top of already losing about 10 points since mid-November."},
    {"question": "What specific issues did Novafives report in their third-quarter results?",
     "answer": "Novafives reported weak third-quarter results with covenant net leverage increasing to 5.8 times, up from 4.8 in the second quarter."},
    {"question": "What financial action did Vue International (VUECIN) take as December approaches?",
     "answer": "Vue International decided to issue a 7-year facility, consisting of two tranches: one in euros amounting to roughly €600 million and one in GBP amounting to £300 million."},
    {"question": "What are the intended uses of the proceeds from Vue International's new term loan?",
     "answer": "The proceeds from the term loan will be used to finance the acquisition of Cinestar and its new site capital expenditures, as well as to refinance existing debt."},
    {"question": "What will happen to VUECIN’s existing debt instruments as a result of the new term loan?",
     "answer": "Vue International’s existing senior secured floating rate notes in euros and senior secured notes in sterling will be entirely taken out."},
    {"question": "How will the new debt issuance affect the deliverables into VUECIN’s CDS contracts?",
     "answer": "Following the issuance, only loans will be deliverable into CDS contracts going forward."},
    {"question": "What was the impact on VUECIN’s leverage due to the new financing?",
     "answer": "Despite the new financing, the leverage is expected to remain flat at 5.5x."},
    {"question": "Why is BATSLN particularly vulnerable to the FDA’s potential regulation on menthol cigarettes?",
     "answer": "BATSLN is particularly vulnerable because it produces Newport, the best-selling brand of menthol cigarettes in the US, and menthol cigarettes constitute about one-fourth of BATSLN's profits."},
    {"question": "How did BATSLN's stock respond to the news of potential FDA restrictions on menthol cigarettes?",
     "answer": "Following the announcement of potential FDA restrictions on menthol cigarettes, BATSLN’s stock price fell by 11%."},
    {"question": "What other factor has contributed to the increased caution from investors towards BATSLN?",
     "answer": "ncreased caution from investors towards BATSLN can also be attributed to the company’s higher leverage, now around 3.7x, which has persisted since the 2017 merger with Reynolds American Inc (RAI)."},
]

len(questions_and_answers)

questions = [dspy.Example(question=elem["question"]).with_inputs("question") for elem in questions_and_answers]

trainset = questions[:25]
devset = questions[25:35]
testset = questions[35:]

len(trainset), len(devset), len(testset)

(25, 10, 20)

Define the evaluation metric

An answer from the RAG system, produced by a language model, can be evaluated by another language model.

A RAG system may generate answers using a smaller faster model (to be reponsive for the user or just cost efficient).

However, if we need to evaluate the answers of the RAG system to benchmark or improve it, we can use a larger slower model.

This is what we are going to do next: The RAG system will be based on GPT 3.5 to generate answers; We will evaluate the quality of the answers using GPT 4.

# language model to generate answers
gpt_turbo = dspy.OpenAI(model="gpt-3.5-turbo", max_tokens=4000)

# language model to evaluate answers
metricLM = dspy.OpenAI(model='gpt-4', max_tokens=1000, model_type='chat')

class Assess(dspy.Signature):
    """Assess the quality of an answer to a question."""

    context = dspy.InputField(desc="The context for answering the question.")
    assessed_question = dspy.InputField(desc="The evaluation criterion.")
    assessed_answer = dspy.InputField(desc="The answer to the question.")
    assessment_answer = dspy.OutputField(desc="A rating between 1 and 5. Only output the rating and nothing else.")

def llm_metric(gold, pred, trace=None):

    predicted_answer = pred.answer
    question = gold.question

    print(f"Test Question: {question}")
    print(f"Predicted Answer: {predicted_answer}")

    detail = "Is the assessed answer detailed?"
    faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context."
    overall = f"Please rate how well this answer answers the question, `{question}` based on the context.\n `{predicted_answer}`"

    with dspy.context(lm=metricLM):
        context = retrieve(question)
        detail = dspy.ChainOfThought(Assess)(context="N/A", assessed_question=detail, assessed_answer=predicted_answer)
        faithful = dspy.ChainOfThought(Assess)(context=context, assessed_question=faithful, assessed_answer=predicted_answer)
        overall = dspy.ChainOfThought(Assess)(context=context, assessed_question=overall, assessed_answer=predicted_answer)

    print(f"Faithful: {faithful.assessment_answer}")
    print(f"Detail: {detail.assessment_answer}")
    print(f"Overall: {overall.assessment_answer}")


    total = float(detail.assessment_answer) + float(faithful.assessment_answer)*2 + float(overall.assessment_answer)

    return total / 5.0

test_example = dspy.Example(question="What do cross encoders do?")
test_pred = dspy.Example(answer="They re-rank documents.")

llm_metric(test_example, test_pred)

Test Question: What do cross encoders do?
Predicted Answer: They re-rank documents.
Faithful: 1
Detail: 1
Overall: 1





0.8

test_example = dspy.Example(question="What business did Conagra Brands Inc. spin off on November 9, 2016?")
test_pred = dspy.Example(answer="Conagra completed the spin-off of Lamb Weston, which is primarily focused on the frozen potato fries business.")

llm_metric(test_example, test_pred)

Test Question: What business did Conagra Brands Inc. spin off on November 9, 2016?
Predicted Answer: Conagra completed the spin-off of Lamb Weston, which is primarily focused on the frozen potato fries business.
Faithful: 5
Detail: 3
Overall: 5





3.6

Given (question, answer), we can inspect the calls (prompts) to the GPT 4 LLM, and the completions (green text), that are performed to evaluate the answer:

metricLM.inspect_history(n=3)

Assess the quality of an answer to a question.

---

Follow the following format.

Context: The context for answering the question.

Assessed Question: The evaluation criterion.

Assessed Answer: The answer to the question.

Reasoning: Let's think step by step in order to ${produce the assessment_answer}. We ...

Assessment Answer: A rating between 1 and 5. Only output the rating and nothing else.

---

Context: N/A

Assessed Question: Is the assessed answer detailed?

Assessed Answer: Conagra completed the spin-off of Lamb Weston, which is primarily focused on the frozen potato fries business.

Reasoning: Let's think step by step in order to[32m produce the assessment answer. We need to consider if the answer provides enough detail to fully answer the question. The answer does provide some detail, such as the company involved (Conagra), the action taken (spin-off), and the focus of the new company (frozen potato fries business). However, it does not provide any additional details such as the reason for the spin-off, the date it occurred, or the impact it had on either company. 

Assessment Answer: 3[0m







Assess the quality of an answer to a question.

---

Follow the following format.

Context: The context for answering the question.

Assessed Question: The evaluation criterion.

Assessed Answer: The answer to the question.

Reasoning: Let's think step by step in order to ${produce the assessment_answer}. We ...

Assessment Answer: A rating between 1 and 5. Only output the rating and nothing else.

---

Context:
[1] «CAG ( Conagra Brands Inc. )  was a 12BUSD revenues food US company based in Chicago. On November 9, 2016, the company completed the spin-off of Lamb Weston - essentially its frozen potato fries  business (a 4BUSD revenue business). French fries and any sort of fried potatoes is a sound investment. Half of the debt of Conagra was pushed to the new company LW (( Lamb Weston Holdings Inc. ). From a BBB- company, the new Conagra is now BBB while  Lamb Weston is a BB. According to last Friday’s ISDA determination committee, a CDS holder having 1MUSD protection on CAG is now having 500k on new CAG and 500k on LW.  New CAG is indicated 20bps tighter while LW is 40bps wider. CAG is also a member of the investment grade credit indices in the US, the CDX.IG, in all series up to the latest the series 27. All credit indices members will also be split and will have 126 reference entities, CAG and LW being half weighted. This credit event is a source of large operations for credit derivatives trade processing in the next days.

Meanwhile, the broader credit market went through a slow session with European investment grade risk being the weakest part of the investment spectrum.
»
[2] «Overnight 3G Capital Partners and Berkshire Hathaway have offered to merge Kraft Foods Group with HNZ (HJ Heinz Company) to create The Kraft Heinz Co. 3G and Berkshire will own 51% on the new company, while existing Kraft shareholders will get 49% and a $16.5 special dividend. Importantly for the CDS market, no new debt will be issued and the management announced that they are committed to maintaining an investment grade rating going forward. The operation is effectively removing the LBO risk which was overhanging on the food and beverage sector as 3G now appears committed (at least in the medium term). So entire complex traded better today, but the most spectacular move was seen in HNZ’s 5 year CDS which gapped 119bps to 64bps as investors expect it to be a dead box going forward.»
[3] «Back in March, METFNL ( Metro AG ) announced that it would proceed with a demerger, in order to separate its food business (Food Co) from  its Consumer Electronics business (CE Co). METFNL held a conference call on the 6th September to give further details regarding the operation. All the financial liabilities of the group including bonds will be assumed by Food Co. Pension liabilities will be allocated 40% to Food Co and 60% to CE Co. Lease obligations will be 60% and 40%, and cash balances 75% and 25% respectively. Even though METFNL’s management expects both companies to maintain an investment grade rating after the spin-off, there won’t be any capital increase. That means that Food Co, due to the high debt load, will have at best weak credit metrics for its rating category and will be left with very limited financial leeway. Moody’s is understood to effectively ask the management to do more if Food Co is to be eligible for IG rating. That is probably why METFNL which will reference Food Co going forward has underperformed its peers during the last week. 
Meanwhile, the broader credit market spent another day looking at US interest rates. It dithered all session, and was unable to decide whether to go wider or tighter. Credit indices traded in a range (328/335 for iTraxx Crossover and 69/72 for iTraxx Main) and eventually closed bang in the middle of it.
»
[4] «The failings of the food industry are in the spotlight again. This morning, ITV News and the Guardian have published a report that claims to have uncovered a series of safety breaches at the poultry plants of 2 Sisters Food Group (2SFG). The allegations involve tampering slaughter date of poultry being processed at sites that supply retailers including Tesco, Sainsbury’s, Marks & Spencer, Aldi and Lidl. The latters all said they would launch enquiries. Several of them decided to remove from their shelves products coming from the incriminated sites without further ado and have vowed to suspend their relationship with the firm until the investigation has concluded. 2SFG was founded in 1993 and now produces one third of all the poultry products consumed in the UK, and had revenues of £3.1Bln in 2016. Investors took the matter very seriously and parent company BOPRLN (Boparan Finance Plc) was under severe pressure as soon as the reports came out. Its 5-year risk premium jumped 169bps to 738bps. That is its widest level since it joined iTraxx Crossover 3 years ago.»
[5] «In an environment which has been supportive for credit over the last 12 months, a few names have been trading wider, especially in the Consumer Non-Cyclical sector. Releveraging seems to be on the agenda of a few companies through different channels. For instance, on the one hand, DGX ( Quest Diagnostics Inc) experienced weak trading conditions and decided to support its share price through share buy-backs. On the other hand, Campbell Soup (Campbell Soup Company) has been the subject of takeover speculations. Out of the money options on the stock experienced unusually high volumes recently and 5 year protection has been pushed aggressively wider. LBO could be back in people’s mind if they think systemic risks are receding.»

Assessed Question: Is the assessed text grounded in the context? Say no if it includes significant facts not in the context.

Assessed Answer: Conagra completed the spin-off of Lamb Weston, which is primarily focused on the frozen potato fries business.

Reasoning: Let's think step by step in order to[32m produce the assessment answer. We can see that the assessed answer is indeed grounded in the context. The first paragraph of the context mentions that Conagra completed the spin-off of Lamb Weston, which is its frozen potato fries business. Therefore, the assessed answer is accurate and does not include any significant facts not in the context.

Assessment Answer: 5[0m







Assess the quality of an answer to a question.

---

Follow the following format.

Context: The context for answering the question.

Assessed Question: The evaluation criterion.

Assessed Answer: The answer to the question.

Reasoning: Let's think step by step in order to ${produce the assessment_answer}. We ...

Assessment Answer: A rating between 1 and 5. Only output the rating and nothing else.

---

Context:
[1] «CAG ( Conagra Brands Inc. )  was a 12BUSD revenues food US company based in Chicago. On November 9, 2016, the company completed the spin-off of Lamb Weston - essentially its frozen potato fries  business (a 4BUSD revenue business). French fries and any sort of fried potatoes is a sound investment. Half of the debt of Conagra was pushed to the new company LW (( Lamb Weston Holdings Inc. ). From a BBB- company, the new Conagra is now BBB while  Lamb Weston is a BB. According to last Friday’s ISDA determination committee, a CDS holder having 1MUSD protection on CAG is now having 500k on new CAG and 500k on LW.  New CAG is indicated 20bps tighter while LW is 40bps wider. CAG is also a member of the investment grade credit indices in the US, the CDX.IG, in all series up to the latest the series 27. All credit indices members will also be split and will have 126 reference entities, CAG and LW being half weighted. This credit event is a source of large operations for credit derivatives trade processing in the next days.

Meanwhile, the broader credit market went through a slow session with European investment grade risk being the weakest part of the investment spectrum.
»
[2] «Overnight 3G Capital Partners and Berkshire Hathaway have offered to merge Kraft Foods Group with HNZ (HJ Heinz Company) to create The Kraft Heinz Co. 3G and Berkshire will own 51% on the new company, while existing Kraft shareholders will get 49% and a $16.5 special dividend. Importantly for the CDS market, no new debt will be issued and the management announced that they are committed to maintaining an investment grade rating going forward. The operation is effectively removing the LBO risk which was overhanging on the food and beverage sector as 3G now appears committed (at least in the medium term). So entire complex traded better today, but the most spectacular move was seen in HNZ’s 5 year CDS which gapped 119bps to 64bps as investors expect it to be a dead box going forward.»
[3] «Back in March, METFNL ( Metro AG ) announced that it would proceed with a demerger, in order to separate its food business (Food Co) from  its Consumer Electronics business (CE Co). METFNL held a conference call on the 6th September to give further details regarding the operation. All the financial liabilities of the group including bonds will be assumed by Food Co. Pension liabilities will be allocated 40% to Food Co and 60% to CE Co. Lease obligations will be 60% and 40%, and cash balances 75% and 25% respectively. Even though METFNL’s management expects both companies to maintain an investment grade rating after the spin-off, there won’t be any capital increase. That means that Food Co, due to the high debt load, will have at best weak credit metrics for its rating category and will be left with very limited financial leeway. Moody’s is understood to effectively ask the management to do more if Food Co is to be eligible for IG rating. That is probably why METFNL which will reference Food Co going forward has underperformed its peers during the last week. 
Meanwhile, the broader credit market spent another day looking at US interest rates. It dithered all session, and was unable to decide whether to go wider or tighter. Credit indices traded in a range (328/335 for iTraxx Crossover and 69/72 for iTraxx Main) and eventually closed bang in the middle of it.
»
[4] «The failings of the food industry are in the spotlight again. This morning, ITV News and the Guardian have published a report that claims to have uncovered a series of safety breaches at the poultry plants of 2 Sisters Food Group (2SFG). The allegations involve tampering slaughter date of poultry being processed at sites that supply retailers including Tesco, Sainsbury’s, Marks & Spencer, Aldi and Lidl. The latters all said they would launch enquiries. Several of them decided to remove from their shelves products coming from the incriminated sites without further ado and have vowed to suspend their relationship with the firm until the investigation has concluded. 2SFG was founded in 1993 and now produces one third of all the poultry products consumed in the UK, and had revenues of £3.1Bln in 2016. Investors took the matter very seriously and parent company BOPRLN (Boparan Finance Plc) was under severe pressure as soon as the reports came out. Its 5-year risk premium jumped 169bps to 738bps. That is its widest level since it joined iTraxx Crossover 3 years ago.»
[5] «In an environment which has been supportive for credit over the last 12 months, a few names have been trading wider, especially in the Consumer Non-Cyclical sector. Releveraging seems to be on the agenda of a few companies through different channels. For instance, on the one hand, DGX ( Quest Diagnostics Inc) experienced weak trading conditions and decided to support its share price through share buy-backs. On the other hand, Campbell Soup (Campbell Soup Company) has been the subject of takeover speculations. Out of the money options on the stock experienced unusually high volumes recently and 5 year protection has been pushed aggressively wider. LBO could be back in people’s mind if they think systemic risks are receding.»

Assessed Question: Please rate how well this answer answers the question, `What business did Conagra Brands Inc. spin off on November 9, 2016?` based on the context. `Conagra completed the spin-off of Lamb Weston, which is primarily focused on the frozen potato fries business.`

Assessed Answer: Conagra completed the spin-off of Lamb Weston, which is primarily focused on the frozen potato fries business.

Reasoning: Let's think step by step in order to[32m produce the assessment answer. We first look at the context provided. The first paragraph clearly states that Conagra Brands Inc. completed the spin-off of Lamb Weston, which is essentially its frozen potato fries business. This directly answers the question asked. The assessed answer is accurate and directly taken from the context, providing the exact information asked for in the question.

Assessment Answer: 5[0m

Generation ‘module’

class GenerateAnswer(dspy.Signature):
    """Answer questions based on the context."""

    context = dspy.InputField(desc="may contain relevant facts")
    question = dspy.InputField()
    answer = dspy.OutputField()

# setting GPT 3.5 as the default lm for the generation in dspy
dspy.settings.configure(lm=gpt_turbo)

# dummy example of the og tutorial, with a code fix on the missing context
dspy.Predict(GenerateAnswer)(question="What are Cross Encoders?", context="Not available.")
dspy.settings.lm.inspect_history(n=1)

Answer questions based on the context.

---

Follow the following format.

Context: may contain relevant facts
Question: ${question}
Answer: ${answer}

---

Context: Not available.
Question: What are Cross Encoders?
Answer: Cross Encoders are a type of neural network architecture used in natural language processing tasks, particularly in sentence pair classification. They take two input sequences and produce a single output, making them suitable for tasks like semantic textual similarity and paraphrase identification.[0m

# same, but with an extra CoT step
dspy.ChainOfThought(GenerateAnswer)(question="What are Cross Encoders?", context="Not available.")
dspy.settings.lm.inspect_history(n=1)

Answer questions based on the context.

---

Follow the following format.

Context: may contain relevant facts

Question: ${question}

Reasoning: Let's think step by step in order to ${produce the answer}. We ...

Answer: ${answer}

---

Context: Not available.

Question: What are Cross Encoders?

Reasoning: Let's think step by step in order to[32m understand what Cross Encoders are. Cross Encoders are a type of neural network architecture commonly used in natural language processing tasks. They are designed to take two input sequences and produce a single output, typically used for tasks like sentence pair classification or similarity scoring.

Answer: Cross Encoders are a type of neural network architecture used in NLP tasks to process two input sequences and produce a single output.[0m

Definition of the RAG system: Retriever + Generation modules

class RAG(dspy.Module):
    def __init__(self, num_passages=3):
        super().__init__()

        self.retrieve = retrieve
        self.generate_answer = dspy.ChainOfThought(GenerateAnswer)

    def forward(self, question):
        context = self.retrieve(question)
        prediction = self.generate_answer(context=context, question=question)
        return dspy.Prediction(answer=prediction.answer)

uncompiled_rag = RAG()

Here is an example of a question that cannot be answered by strictly adhering to the given corpus (and retrieved context):

print(uncompiled_rag("What are re-rankers in search engines?").answer)

The context provided does not mention re-rankers in search engines. Therefore, we cannot provide an answer to this question based on the given information.

dspy.settings.lm.inspect_history(n=1)

Answer questions based on the context.

---

Follow the following format.

Context: may contain relevant facts

Question: ${question}

Reasoning: Let's think step by step in order to ${produce the answer}. We ...

Answer: ${answer}

---

Context:
[1] «The session turned out to be fairly strong across the board, and there were only a handful of names which closed wider on the day. The tone was a bit more hesitant in the morning though, as people feared that the bank holiday in the US tomorrow would weight on liquidity. In the early hours of trading, RENAUL ( Renault SA ) was one of the standout names. It started the day strongly on the back of an upgrade by Fitch. The rating agency revised RENAUL’s rating to BBB- outlook positive, bringing it back in the investment grade category. The 5 year CDS quickly tightened by 15bps (it closed at 112.5bps) as buyers of cash started to emerge. Indeed, if this upgrade took place earlier that investors had anticipated, they now expect S&P; and Moodys to follow suit. If that happens, investment grade funds will start to buy cash, which should lead to further outperformance. Rating actions still matter, especially for names that sit on the border of investment grade and high yield. »
[2] «European synthetic credit market added in March 11 new high yield  reference entities to its trading catalog. The new names represent 11 out of the 12 additions to the new series of the European Crossover index. 
  The last one addition is a fallen angel (TDC A/S).  After a month, 
  the roll was march the 20th, the trading of these new names is still rather subdued and a sort 
  of disappointment for market participants. It is hard to assess the true activity. 
  The tip of the Iceberg is the reported cleared volume by LCH CDSCLear, 
  the only CDS clearing house offering to clear these new names. 
    Out of the 11 names, WIND TRE S.P.A. and Verisure Midholding AB are the only one with reported 
    open interests as of yesterday according to OTCStreaming. 
    Some of the new names are well known special situations in the European high yield community. 
    These entities have large bond issues like Picard Bondco S.A. with a 1.2BEuros 5Y FRN 
    issued late last year. But the derivatives community is not familiar with these names
     especially due to the lack of time series. DataGrapple infers for these entities an 
     alleged time series using the "best" benchmark. The choice of the benchmark is not obvious. 
     In some situation a benchmark is not relevant. For STEINHOFF EUROPE AG, Datagrapple team decided 
     to use NEW LOOK SENIOR ISSUER PLC as the best proxy to backward infer a time series (see the attached
 the grapple). DataGrapple provides estimates for all the new CDS entities, hopefully it helps to make some rough estimate  of their risks.»
[3] «When the composition of the Crossover is modified, the common belief is that new entrants will suffer as they are now part of the hedging tool most widely used by portfolio managers, and that names existing the index will benefit. This Grapple paints a slightly different picture, and shows that this time around, if the exiting names are among the best performers since the beginning of the week, the new entrants are also part of this elite group. Dealers might have been a bit harsh on them on Monday anticipating high demand for protection, and these high risk premia triggered some clients’ selling of CDS. The hunt for yield is still on, and some of the names joining the index have decent credit metrics which makes them interesting portfolio diversifying options.»
[4] «The roll and the launch of the new series of indices have not brought a sea of change to the credit market. On the one hand, credit indices remain an investment vehicle of choice and most index rolls between new series and the previous ones trade below their fair value (with the exception of iTraxx Financials in Europe where clients are still short risk). This means that new series are more expensive (i.e. they trade tighter relative to their values) than the previous ones. On the other hand, there are numerous stories affecting single names. The commodities’ saga is still unfolding, Emerging Markets are a concern to investors, and the automotive sector is having a brutal time (particularly in Europe) to name a few. So one should not be surprised to see the negative index credit bases environment persist for some time.»
[5] «You might struggle to see all of them on this grapple, but we are pleased to announce that the universe captured by DataGrapple has now been slightly expanded: the number of entities on which prices and volumes are available has increased from 680 to 697, mainly on the back on new inclusions in the indices launched last Friday (we also slightly anticipated the launch of CDXHY series 24). In addition to the index constituents, DataGrapple still references the 500 most actively traded reference entities over the last 6 months according to DTTC. Rating changes have been factored in, and the relevant trees now reflect this of course. We also put a great amount of work on improving our clustering algorithm. Based on our 9-year-long time series, it produces all the Machine Trees which regroup statistically close entities. They should be much more stable going forward. As always comments and questions are welcome, so please get in touch with us.»

Question: What are re-rankers in search engines?

Reasoning: Let's think step by step in order to[32m produce the answer. We need to understand the context provided and look for any mention of re-rankers in search engines.

Answer: The context provided does not mention re-rankers in search engines. Therefore, we cannot provide an answer to this question based on the given information.[0m

Here is an example of a question that can be answered using the provided blogs:

print(uncompiled_rag("What is the intended purpose of the capital increase by Banca Monte dei Paschi?").answer)

The intended purpose of the capital increase by Banca Monte dei Paschi is to strengthen the bank's financial position and address its bad loan portfolio by deconsolidating almost €28 billion in non-performing loans.

dspy.settings.lm.inspect_history(n=1)

Answer questions based on the context.

---

Follow the following format.

Context: may contain relevant facts

Question: ${question}

Reasoning: Let's think step by step in order to ${produce the answer}. We ...

Answer: ${answer}

---

Context:
[1] «Over the last fortnight, MONTE’s ( Banca Monte dei Paschi  ) stock has roughly doubled. The 5-year risk premium of its senior debt has been cut by 125bps to 350bps since late September. This morning, it looked as if this amazing ride could continue for a while. The company outlined plans to eliminate 2,600 jobs, shut 500 branches and slash the lender's €28Bln pile of bad loans. That sent the stock another 20% up and the debt risk premium another 20bps tighter. To complete its turnaround, the bank also needs to raise €5Bln in fresh capital by year end though, and the CEO admitted that his effort had only begun in that respect. The plan involves a voluntary debt to equity swap, in order to cut the amount MONTE would need to raise from shareholders. The bank wants to spin off €28Bln of souring loan, swap debt for equity and manage a capital increase all at the same time in an exceptionally compressed timeframe. To make things trickier still, there is the December 4 constitutional referendum in Italy which could lead to a increased volatility towards the end of the year. When that reality hit investors, they sent the stock 30% down from its - 15% down on the day  -and the risk premium back to almost unchanged on the day.
 »
[2] «Once the results of the Italian referendum were known, MONTE’s ( Banca Monte dei Paschi  ) board asked to the ECB an extension from the end of the year to Jan. 20 of the deadline set for its recapitalisation exercise “due to the changed reference context”. A delay would have allowed the bank more time to find investors while Italian leaders put a new government in place following the resignation of Prime Minister Matteo Renzi. Early in the afternoon, it emerged that the ECB rejected the bank’s request, increasing the likelihood of a state bailout that would impose losses on shareholders and bondholders. Up to that point, the credit market had been back on the bullish trajectory it has followed since Monday. If the news was not enough to derail the whole market (iTraxx Main closed 1.5bps at 72.5bps and iTraxx Crossover 8bps tighter at 306.5bps), it reversed the trend of iTraxx Financial Senior and Subordinated which closed 1bps wider at 98bps (2bps off the intraday tights) and 3.5bps wider at 225.5bps (12bps off the intraday tights) respectively. On Monday, we might learn how systemically important MONTE really is…»
[3] «Italian banks have been much talked about over the last few sessions. Positive vibes first came during the week-end when Banca Popolare Di Milano and BPIM ( Banca Popolare SC ) won shareholders’ approval for a merger creating Italy’s third largest lender. Today, MONTE’s ( Banca Monte dei Paschi  ) board met after asking their CEO to look into a proposal by Mr Passera, Italy’s ex-minister for economic development and former CEO of Intesa Sanpaolo, to shore up its finances. The plan envisions a €5Bln capital increase, which would include a €1Bln share sale to existing shareholders and €2.5Bln from new long terms backers. While volumes traded on peripheral banks are still dwarfed by transactions on DB, these string of positive news enabled them to outperformed their peers recently and they are the brightest green spots in the above grapple.

Meanwhile, the broader credit market also benefitted from the better tone in the financial sector and from a few earnings beats which buoyed the equity market. Until tomorrow night, iTraxx Main and CDXIG could find it difficult to trade significantly away from their current levels (72.5bps and 75bps respectively) as these are important pins for options expiring late in the afternoon.
»
[4] «MONTE ( Banca Monte dei Paschi  ) gave some details about their debt for equity exchange yesterday night. They plan to offer equity worth between 85% and 100% of face value to the holders of their €4.3Bln outstanding subordinated bonds. The generous terms reflect the need to deliver on this transaction as fully as possible. Indeed, they want to keep the amount of fresh external equity to be raised to a minimum, as the €5Bln total capital increase planned before the end of the year is instrumental in a process that should lead to the deconsolidation of their almost €28Bln portfolio of non-performing loans. Renewed confidence in the success of the deal led investors to send MONTE’s 5-year risk premium 12bps tighter to 392bps, and benefitted the whole Italian complex which outperformed other members of the European financial sector.

Meanwhile, the broader credit market benefitted from the stabilisation of interest rates in Europe and in the US which held onto their overnight (modest) gains throughout the session. iTraxx Crossover and CDX High Yield were the outperformers (closing 9bps and 16bps tighter at 341bps and 418bps respectively), while iTraxx Main (ITXEB) and CDX IG closed 1.5bps and 3bps tighter at 77.5bps and 75bps respectively. Tomorrow is option expiry day on credit indices, and chances are that ITXEB will be attracted by the 75bps strike which represents an important option pin.
»
[5] «On Friday, it emerged that Fortress Investment Group and Elliott Capital Management had dropped out of talks to buy bad loans from MONTE ( Banca Monte dei Paschi  ) complicating the rescue plan for the lender backed by the Italian government. They were the only international bidders for the riskier tranches of MONTE’s bad loan securitization. That leaves Atlante, the fund set up to help the struggling Italian banking sector, as the only potential buyer and jeopardizes the asset sale, which is a key part of the plan to restructure the bank with a capital injection from the state, after MONTE failed to shore up capital privately. Ultimately, it could also make similar rescue plans for two other northern Italian lenders, Veneto Banca Spa and Popolare Vicenza Spa, much more difficult to pull off. Surprisingly, if MONTE’s 5-year risk premium was marked aggressively wider - insuring senior debt now costs 330bps per year, while insuring subordinated costs 73.5% upfront -, it did not feed through the whole Italian banking sector and most names were actually unchanged to a tad tighter.»

Question: What is the intended purpose of the capital increase by Banca Monte dei Paschi?

Reasoning: Let's think step by step in order to[32m produce the answer. We know from the context that Banca Monte dei Paschi plans to raise €5 billion in fresh capital by the end of the year. This capital increase is instrumental in a process that should lead to the deconsolidation of their almost €28 billion portfolio of non-performing loans. The bank also plans to offer equity to holders of their outstanding subordinated bonds as part of a debt-to-equity exchange. Therefore, the intended purpose of the capital increase is to strengthen the bank's financial position and address its bad loan portfolio.

Answer: The intended purpose of the capital increase by Banca Monte dei Paschi is to strengthen the bank's financial position and address its bad loan portfolio by deconsolidating almost €28 billion in non-performing loans.[0m

We can evaluate this crude RAG (un-optimized prompts) on the (validation) devset, and obtain a baseline score:

from dspy.evaluate.evaluate import Evaluate

evaluate = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)

evaluate(RAG(), metric=llm_metric)

  0%|          | 0/10 [00:00<?, ?it/s]

Test Question: What sectors does NXP Semiconductors NV primarily supply chips for?
Predicted Answer: NXP Semiconductors NV primarily supplies chips for the smartphone and auto industries.
Faithful: 5

Average Metric: 3.4 / 1  (340.0):  10%|█         | 1/10 [00:00<00:03,  2.35it/s]


Detail: 2
Overall: 5
Test Question: What are some of the applications of NXP's chips in the auto industry?
Predicted Answer: Some of the applications of NXP's chips in the auto industry include advanced driver assistance systems, "infotainment," and in-vehicle networking between different car systems.


Average Metric: 7.4 / 2  (370.0):  20%|██        | 2/10 [00:00<00:03,  2.16it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: How did NXP's Q1 2016 earnings report compare to analysts' expectations?
Predicted Answer: NXP's Q1 2016 earnings report was in line with analysts' expectations, and the company managed to surprise investors with their numbers.


Average Metric: 11.0 / 3  (366.7):  30%|███       | 3/10 [00:01<00:03,  2.25it/s]

Faithful: 5
Detail: 3
Overall: 5
Test Question: What did NXP's management say about the demand and market conditions during their earnings report?
Predicted Answer: The management of NXP stated that overall demand continues to be subdued, but they also mentioned that the headwinds experienced in the previous year should begin to subside in the coming quarter.


Average Metric: 15.0 / 4  (375.0):  40%|████      | 4/10 [00:01<00:02,  2.12it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What was NXP's revenue and gross margin outlook for Q2, and how did it compare to the consensus estimates?
Predicted Answer: The revenue outlook for Q2 was $2.3-2.4 billion, slightly above the consensus of $2.3 billion, and the gross margin outlook was 49.5-50.5%, slightly above the consensus of 49.8%.


Average Metric: 19.0 / 5  (380.0):  50%|█████     | 5/10 [00:02<00:02,  2.15it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What steps has Ball taken to win regulatory approval for its acquisition of Rexam?
Predicted Answer: Ball has agreed with Rexam to sell plants in Brazil and Europe, along with innovation and support functions in several countries, to ARGID (Ardagh Packaging) to satisfy regulatory concerns. This divestment program is subject to regulatory approvals and will enable ARGID to acquire assets with combined revenues of around $3 billion. Additionally, ARGID will issue $2.85 billion of secured and unsecured notes to help finance the operation.


Average Metric: 23.0 / 6  (383.3):  60%|██████    | 6/10 [00:02<00:01,  2.06it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: Who is acquiring the assets divested by Ball and Rexam, and what is the significance of this acquisition?
Predicted Answer: ARGID (Ardagh Packaging) is acquiring the assets divested by Ball and Rexam. This acquisition is significant as it will allow ARGID to acquire assets with combined revenues of around $3 billion, costing the company approximately $3.4 billion, as part of a divestment program to satisfy regulatory concerns related to the Ball-Rexam deal.


Average Metric: 27.0 / 7  (385.7):  70%|███████   | 7/10 [00:03<00:01,  1.98it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: Why did ARGID's risk premium not participate in the recent rally of the iTraxx Crossover index?
Predicted Answer: ARGID's risk premium did not participate in the recent rally of the iTraxx Crossover index because it has been dropped from the index and is no longer included in the group of entities being traded.


Average Metric: 27.0 / 8  (337.5):  80%|████████  | 8/10 [00:04<00:01,  1.74it/s]

Faithful: No
Detail: 5
Overall: 1
Error for example in dev set:          could not convert string to float: 'No'
Test Question: How did the market react to the announcement regarding ARGID's involvement in the Ball-Rexam deal?
Predicted Answer: The market reacted to the announcement regarding ARGID's involvement in the Ball-Rexam deal by marking ARGID's 5-year CDS 36bps wider to 452bps.


Average Metric: 31.0 / 9  (344.4):  90%|█████████ | 9/10 [00:04<00:00,  1.77it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What adjustments did Alcoa Inc make to its global aluminum demand forecast for 2016?
Predicted Answer: Alcoa Inc adjusted their global aluminum demand forecast for 2016 from a 6% increase to a 5% climb.


Average Metric: 34.8 / 10  (348.0): 100%|██████████| 10/10 [00:05<00:00,  1.90it/s]

Faithful: 5
Detail: 4
Overall: 5
Average Metric: 34.8 / 10  (348.0%)

	question	answer	llm_metric
0	What sectors does NXP Semiconductors NV primarily supply chips for?	NXP Semiconductors NV primarily supplies chips for the smartphone and auto industries.	3.4
1	What are some of the applications of NXP's chips in the auto industry?	Some of the applications of NXP's chips in the auto industry include advanced driver assistance systems, "infotainment," and in-vehicle networking between different car systems.	4.0
2	How did NXP's Q1 2016 earnings report compare to analysts' expectations?	NXP's Q1 2016 earnings report was in line with analysts' expectations, and the company managed to surprise investors with their numbers.	3.6
3	What did NXP's management say about the demand and market conditions during their earnings report?	The management of NXP stated that overall demand continues to be subdued, but they also mentioned that the headwinds experienced in the previous year should...	4.0
4	What was NXP's revenue and gross margin outlook for Q2, and how did it compare to the consensus estimates?	The revenue outlook for Q2 was $2.3-2.4 billion, slightly above the consensus of $2.3 billion, and the gross margin outlook was 49.5-50.5%, slightly above the...	4.0

... 5 more rows not displayed ...

348.0

Using DSPy to optimize the RAG system with `BootstrapFewShot`

from dspy.teleprompt import BootstrapFewShot

teleprompter = BootstrapFewShot(metric=llm_metric, max_labeled_demos=8, max_rounds=3)

compiled_rag = teleprompter.compile(uncompiled_rag, trainset=trainset)

  0%|          | 0/25 [00:00<?, ?it/s]

Test Question: What business did Conagra Brands Inc. spin off on November 9, 2016?
Predicted Answer: Conagra Brands Inc. spun off Lamb Weston on November 9, 2016.


  4%|▍         | 1/25 [00:00<00:09,  2.52it/s]

Faithful: 5
Detail: 5
Overall: 5


  8%|▊         | 2/25 [00:00<00:09,  2.52it/s]

Test Question: How much of Conagra's debt was transferred to Lamb Weston after the spin-off?
Predicted Answer: Half of Conagra's debt was transferred to Lamb Weston after the spin-off.
Faithful: 5
Detail: 3
Overall: 5
Test Question: What was the revenue size of the spun-off business, Lamb Weston?
Predicted Answer: The revenue size of the spun-off business, Lamb Weston, was 4 billion USD.


 12%|█▏        | 3/25 [00:01<00:09,  2.42it/s]

Faithful: 5
Detail: 2
Overall: 5
Test Question: What were the credit ratings for Conagra and Lamb Weston after the spin-off?
Predicted Answer: Conagra's credit rating after the spin-off was BBB, and Lamb Weston's credit rating was BB.


 16%|█▌        | 4/25 [00:01<00:09,  2.32it/s]


Faithful: 5
Detail: 5
Overall: 5


  0%|          | 0/25 [00:00<?, ?it/s]
  0%|          | 0/25 [00:00<?, ?it/s]

Bootstrapped 4 full traces after 1 examples in round 2.

compiled_rag("What is the intended purpose of the capital increase by Banca Monte dei Paschi?").answer

'The intended purpose of the capital increase by Banca Monte dei Paschi is to lead to the deconsolidation of their almost €28 billion portfolio of non-performing loans.'

If we inspect the LLM call, we can observe that the prompt is now much longer and contains several examples of (context, question, reasoning, answer) before the completion of the actual expected answer:

dspy.settings.lm.inspect_history(n=1)

Answer questions based on the context.

---

Follow the following format.

Context: may contain relevant facts

Question: ${question}

Reasoning: Let's think step by step in order to ${produce the answer}. We ...

Answer: ${answer}

---

Context:
[1] «CAG ( Conagra Brands Inc. )  was a 12BUSD revenues food US company based in Chicago. On November 9, 2016, the company completed the spin-off of Lamb Weston - essentially its frozen potato fries  business (a 4BUSD revenue business). French fries and any sort of fried potatoes is a sound investment. Half of the debt of Conagra was pushed to the new company LW (( Lamb Weston Holdings Inc. ). From a BBB- company, the new Conagra is now BBB while  Lamb Weston is a BB. According to last Friday’s ISDA determination committee, a CDS holder having 1MUSD protection on CAG is now having 500k on new CAG and 500k on LW.  New CAG is indicated 20bps tighter while LW is 40bps wider. CAG is also a member of the investment grade credit indices in the US, the CDX.IG, in all series up to the latest the series 27. All credit indices members will also be split and will have 126 reference entities, CAG and LW being half weighted. This credit event is a source of large operations for credit derivatives trade processing in the next days.

Meanwhile, the broader credit market went through a slow session with European investment grade risk being the weakest part of the investment spectrum.
»
[2] «Overnight 3G Capital Partners and Berkshire Hathaway have offered to merge Kraft Foods Group with HNZ (HJ Heinz Company) to create The Kraft Heinz Co. 3G and Berkshire will own 51% on the new company, while existing Kraft shareholders will get 49% and a $16.5 special dividend. Importantly for the CDS market, no new debt will be issued and the management announced that they are committed to maintaining an investment grade rating going forward. The operation is effectively removing the LBO risk which was overhanging on the food and beverage sector as 3G now appears committed (at least in the medium term). So entire complex traded better today, but the most spectacular move was seen in HNZ’s 5 year CDS which gapped 119bps to 64bps as investors expect it to be a dead box going forward.»
[3] «Back in March, METFNL ( Metro AG ) announced that it would proceed with a demerger, in order to separate its food business (Food Co) from  its Consumer Electronics business (CE Co). METFNL held a conference call on the 6th September to give further details regarding the operation. All the financial liabilities of the group including bonds will be assumed by Food Co. Pension liabilities will be allocated 40% to Food Co and 60% to CE Co. Lease obligations will be 60% and 40%, and cash balances 75% and 25% respectively. Even though METFNL’s management expects both companies to maintain an investment grade rating after the spin-off, there won’t be any capital increase. That means that Food Co, due to the high debt load, will have at best weak credit metrics for its rating category and will be left with very limited financial leeway. Moody’s is understood to effectively ask the management to do more if Food Co is to be eligible for IG rating. That is probably why METFNL which will reference Food Co going forward has underperformed its peers during the last week. 
Meanwhile, the broader credit market spent another day looking at US interest rates. It dithered all session, and was unable to decide whether to go wider or tighter. Credit indices traded in a range (328/335 for iTraxx Crossover and 69/72 for iTraxx Main) and eventually closed bang in the middle of it.
»
[4] «The failings of the food industry are in the spotlight again. This morning, ITV News and the Guardian have published a report that claims to have uncovered a series of safety breaches at the poultry plants of 2 Sisters Food Group (2SFG). The allegations involve tampering slaughter date of poultry being processed at sites that supply retailers including Tesco, Sainsbury’s, Marks & Spencer, Aldi and Lidl. The latters all said they would launch enquiries. Several of them decided to remove from their shelves products coming from the incriminated sites without further ado and have vowed to suspend their relationship with the firm until the investigation has concluded. 2SFG was founded in 1993 and now produces one third of all the poultry products consumed in the UK, and had revenues of £3.1Bln in 2016. Investors took the matter very seriously and parent company BOPRLN (Boparan Finance Plc) was under severe pressure as soon as the reports came out. Its 5-year risk premium jumped 169bps to 738bps. That is its widest level since it joined iTraxx Crossover 3 years ago.»
[5] «In an environment which has been supportive for credit over the last 12 months, a few names have been trading wider, especially in the Consumer Non-Cyclical sector. Releveraging seems to be on the agenda of a few companies through different channels. For instance, on the one hand, DGX ( Quest Diagnostics Inc) experienced weak trading conditions and decided to support its share price through share buy-backs. On the other hand, Campbell Soup (Campbell Soup Company) has been the subject of takeover speculations. Out of the money options on the stock experienced unusually high volumes recently and 5 year protection has been pushed aggressively wider. LBO could be back in people’s mind if they think systemic risks are receding.»

Question: What business did Conagra Brands Inc. spin off on November 9, 2016?

Reasoning: Let's think step by step in order to produce the answer. We know from the context that Conagra Brands Inc. completed the spin-off of Lamb Weston on November 9, 2016.

Answer: Conagra Brands Inc. spun off Lamb Weston on November 9, 2016.

---

Context:
[1] «CAG ( Conagra Brands Inc. )  was a 12BUSD revenues food US company based in Chicago. On November 9, 2016, the company completed the spin-off of Lamb Weston - essentially its frozen potato fries  business (a 4BUSD revenue business). French fries and any sort of fried potatoes is a sound investment. Half of the debt of Conagra was pushed to the new company LW (( Lamb Weston Holdings Inc. ). From a BBB- company, the new Conagra is now BBB while  Lamb Weston is a BB. According to last Friday’s ISDA determination committee, a CDS holder having 1MUSD protection on CAG is now having 500k on new CAG and 500k on LW.  New CAG is indicated 20bps tighter while LW is 40bps wider. CAG is also a member of the investment grade credit indices in the US, the CDX.IG, in all series up to the latest the series 27. All credit indices members will also be split and will have 126 reference entities, CAG and LW being half weighted. This credit event is a source of large operations for credit derivatives trade processing in the next days.

Meanwhile, the broader credit market went through a slow session with European investment grade risk being the weakest part of the investment spectrum.
»
[2] «Back in March, METFNL ( Metro AG ) announced that it would proceed with a demerger, in order to separate its food business (Food Co) from  its Consumer Electronics business (CE Co). METFNL held a conference call on the 6th September to give further details regarding the operation. All the financial liabilities of the group including bonds will be assumed by Food Co. Pension liabilities will be allocated 40% to Food Co and 60% to CE Co. Lease obligations will be 60% and 40%, and cash balances 75% and 25% respectively. Even though METFNL’s management expects both companies to maintain an investment grade rating after the spin-off, there won’t be any capital increase. That means that Food Co, due to the high debt load, will have at best weak credit metrics for its rating category and will be left with very limited financial leeway. Moody’s is understood to effectively ask the management to do more if Food Co is to be eligible for IG rating. That is probably why METFNL which will reference Food Co going forward has underperformed its peers during the last week. 
Meanwhile, the broader credit market spent another day looking at US interest rates. It dithered all session, and was unable to decide whether to go wider or tighter. Credit indices traded in a range (328/335 for iTraxx Crossover and 69/72 for iTraxx Main) and eventually closed bang in the middle of it.
»
[3] «Overnight 3G Capital Partners and Berkshire Hathaway have offered to merge Kraft Foods Group with HNZ (HJ Heinz Company) to create The Kraft Heinz Co. 3G and Berkshire will own 51% on the new company, while existing Kraft shareholders will get 49% and a $16.5 special dividend. Importantly for the CDS market, no new debt will be issued and the management announced that they are committed to maintaining an investment grade rating going forward. The operation is effectively removing the LBO risk which was overhanging on the food and beverage sector as 3G now appears committed (at least in the medium term). So entire complex traded better today, but the most spectacular move was seen in HNZ’s 5 year CDS which gapped 119bps to 64bps as investors expect it to be a dead box going forward.»
[4] «GNW ( Genworth Financial Inc ) offers insurance, wealth management, investment and financial solutions. It products covers mortgage guaranty, life insurance and long-term care insurance. After it repaid its bonds that came due in May, analysts estimated that the company was left with only $560mln of cash while it faces $1.6Bln of debt maturing in 2020-2021. Since October 2016, GNW has been working on clearing its proposed takeover by China Oceanwide. The transaction was recently revamped to include a $1.5Bln capital contribution that would be used mostly to repay GNW’s debt. News over the week-end that the Committee on Foreign Investments in the United States approved the deal after concluding that there are no unresolved security concerns came as a major relief to debt holders. It significantly increases the possibility that GNW’s $2.7Bln sale will go through, which would effectively put the issue of GNW’s bankruptcy that has been on investors’ mind off the agenda. GNW’s 5-year risk premium decreased by 200bps to 425bps, the tightest it has been since mid-2015.»
[5] «A fortnight ago, SLE (Hillshire Brands Co) announced to the market their intention to acquire Pinnacle Foods for an enterprise value of $6bln. With the prospects of more leverage looming, investors sent the 5 year risk premium through the roof and it traded all the way up from 90bps to 140bps in very healthy volumes. Today,  Pilgrim's Pride Corp , a US unit of Brazilian meat company  JBS SA , made an unsolicited $6.4bln takeover offer (or $45/share) for SLE in order to derail their expansion plan. The stock of SLE traded instantaneously at the $45 offer level (22% higher than the level where  SLE’s stock settled post the Pinnacle deal announcement), indicating that investors believe in the success of this hostile take-over, and that they consider the Pinnacle deal dead. The press release indicated that the financing would be done through PPC, which seems to read that SLE would become an orphan reference entity. The reaction from the credit market was almost as fast, and the 5 year CDS of SLE traded all the way back to 90bps. It will eventually go lower once the deal is done and dusted.»

Question: How much of Conagra's debt was transferred to Lamb Weston after the spin-off?

Reasoning: Let's think step by step in order to determine the amount of debt transferred to Lamb Weston. From the context, we know that half of Conagra's debt was pushed to Lamb Weston after the spin-off.

Answer: Half of Conagra's debt was transferred to Lamb Weston after the spin-off.

---

Context:
[1] «CAG ( Conagra Brands Inc. )  was a 12BUSD revenues food US company based in Chicago. On November 9, 2016, the company completed the spin-off of Lamb Weston - essentially its frozen potato fries  business (a 4BUSD revenue business). French fries and any sort of fried potatoes is a sound investment. Half of the debt of Conagra was pushed to the new company LW (( Lamb Weston Holdings Inc. ). From a BBB- company, the new Conagra is now BBB while  Lamb Weston is a BB. According to last Friday’s ISDA determination committee, a CDS holder having 1MUSD protection on CAG is now having 500k on new CAG and 500k on LW.  New CAG is indicated 20bps tighter while LW is 40bps wider. CAG is also a member of the investment grade credit indices in the US, the CDX.IG, in all series up to the latest the series 27. All credit indices members will also be split and will have 126 reference entities, CAG and LW being half weighted. This credit event is a source of large operations for credit derivatives trade processing in the next days.

Meanwhile, the broader credit market went through a slow session with European investment grade risk being the weakest part of the investment spectrum.
»
[2] «A fortnight ago, SLE (Hillshire Brands Co) announced to the market their intention to acquire Pinnacle Foods for an enterprise value of $6bln. With the prospects of more leverage looming, investors sent the 5 year risk premium through the roof and it traded all the way up from 90bps to 140bps in very healthy volumes. Today,  Pilgrim's Pride Corp , a US unit of Brazilian meat company  JBS SA , made an unsolicited $6.4bln takeover offer (or $45/share) for SLE in order to derail their expansion plan. The stock of SLE traded instantaneously at the $45 offer level (22% higher than the level where  SLE’s stock settled post the Pinnacle deal announcement), indicating that investors believe in the success of this hostile take-over, and that they consider the Pinnacle deal dead. The press release indicated that the financing would be done through PPC, which seems to read that SLE would become an orphan reference entity. The reaction from the credit market was almost as fast, and the 5 year CDS of SLE traded all the way back to 90bps. It will eventually go lower once the deal is done and dusted.»
[3] «A month ago, there was a first alert on MRWLN (Wm. Morrisson Supermarkets Plc) when Elliott Associates built a stake in the company. Rumours that a leveraging of the company via a spin-off of their property assets was in the cards were flying around back then. The 5 year CDS never recovered completely and it only participate reluctantly in the recent rally. Today, it took a second leg wider when it was said that the founding family had approached private equity funds such as CVC Capital and Carlyle. Analysts are divided on the feasibility of what would be a $13bln deal, but the market decided it was better safe than sorry and the name was marked 20bps wider (i.e. 1% in cash price) on the day.»
[4] «WHR ( Whirlpool Corporation ) got punished today for missing analysts’ forecasts with third quarter profits. Despite sales rising to $5.42bln – up from $5.24bln last year -, earnings per share fell short 7cts short of expectations at $3.83. The company also lowered its full year projection to $13.6 to $13.9, down from $15. The outlook for the full year 2017 renewed concerns that WHR struggles to integrate Indesit SpA - in which it bought a majority stake roughly 3 years ago – and cannot keep its costs in check. To add insult to injury, it also emerged that Sears has decided to end a century-long partnership and will stop stocking products from Maytag, KitchenAid and Jenn-Air. The retailer balked at paying WHR higher prices, arguing that it would no longer be able to sell Whirlpool products at a competitive price if it acquiesced to the latest demands from the white good maker. WHR’s stock took a bath and was down 10%, while its 5-year risk premium shot up 7bps to 70bps.  »
[5] «Roughly a year ago, SPLS’s (Staples Inc) attempt to buy ODP (Office Depot Inc) for $6.3Bln was thwarted by antitrust regulators. A tough year ensued after its CEO stepped down, during which the company scrambled for a plan B, closing stores and seeking to recast itself as a source of business services. It looks as if these transformation efforts seduced Sycamore though. They announced yesterday night that they are ready to bid $6.9Bln to buy SPLS in what could be the largest LBO announced this year. Even though a deal had been rumoured for some time – it was reported in May that a takeover offer from Cerberus had been rejected because it was too low -, investors initially sent SPLS’s 5-year risk premium soaring 50bps wider at 350bps, as such deals inevitably mean more debt. But soon it transpired that Sycamore, in a similar move to what it did when it bought Jones Group in 2014 and split it in 4 different independent operating companies, could divide SPLS into three different entities: US retail, Canadian retail and corporate-supply business. It inevitably raised the question of where the debt will sit and which entity (or entities) CDS currently referencing SPLS will cover. The answer is not necessarily the most leveraged. The CDS gave up all its widening and more, to close 25bps tighter on the day at 272bps. »

Question: What was the revenue size of the spun-off business, Lamb Weston?

Reasoning: Let's think step by step in order to produce the answer. We know from the context that Lamb Weston was essentially the frozen potato fries business spun off from Conagra Brands Inc. The context also mentions that Lamb Weston had a revenue size of 4 billion USD.

Answer: The revenue size of the spun-off business, Lamb Weston, was 4 billion USD.

---

Context:
[1] «CAG ( Conagra Brands Inc. )  was a 12BUSD revenues food US company based in Chicago. On November 9, 2016, the company completed the spin-off of Lamb Weston - essentially its frozen potato fries  business (a 4BUSD revenue business). French fries and any sort of fried potatoes is a sound investment. Half of the debt of Conagra was pushed to the new company LW (( Lamb Weston Holdings Inc. ). From a BBB- company, the new Conagra is now BBB while  Lamb Weston is a BB. According to last Friday’s ISDA determination committee, a CDS holder having 1MUSD protection on CAG is now having 500k on new CAG and 500k on LW.  New CAG is indicated 20bps tighter while LW is 40bps wider. CAG is also a member of the investment grade credit indices in the US, the CDX.IG, in all series up to the latest the series 27. All credit indices members will also be split and will have 126 reference entities, CAG and LW being half weighted. This credit event is a source of large operations for credit derivatives trade processing in the next days.

Meanwhile, the broader credit market went through a slow session with European investment grade risk being the weakest part of the investment spectrum.
»
[2] «Back in March, METFNL ( Metro AG ) announced that it would proceed with a demerger, in order to separate its food business (Food Co) from  its Consumer Electronics business (CE Co). METFNL held a conference call on the 6th September to give further details regarding the operation. All the financial liabilities of the group including bonds will be assumed by Food Co. Pension liabilities will be allocated 40% to Food Co and 60% to CE Co. Lease obligations will be 60% and 40%, and cash balances 75% and 25% respectively. Even though METFNL’s management expects both companies to maintain an investment grade rating after the spin-off, there won’t be any capital increase. That means that Food Co, due to the high debt load, will have at best weak credit metrics for its rating category and will be left with very limited financial leeway. Moody’s is understood to effectively ask the management to do more if Food Co is to be eligible for IG rating. That is probably why METFNL which will reference Food Co going forward has underperformed its peers during the last week. 
Meanwhile, the broader credit market spent another day looking at US interest rates. It dithered all session, and was unable to decide whether to go wider or tighter. Credit indices traded in a range (328/335 for iTraxx Crossover and 69/72 for iTraxx Main) and eventually closed bang in the middle of it.
»
[3] «Over the last week, the credit market in the US has been very stable, especially in the investment grade universe. The fair value of the CDX IG 5y series 22 has barely moved, going from 63.1bps to 62.7bps. This index has moved a tad more quickly as the basis is now in negative territory and heads towards the 20cts level. But that stability at a macro level masks some dispersion when you drill down to individual companies. If most of the names were well behaved, others have experienced wild swings on the back of M&A; activity. SLE (Hillshire Brands Co) was the main casualty after the company announced their intention to acquire Pinnacle Foods Inc for $6.6bln raising questions regarding their future leverage and pushing the 5 year CDS 55bps wider, from 87.5bps to 142.5bps. At the other end of the spectrum, DTV (Direc TV) was the star performer after SBC (AT&T; Inc) said they are in talks to acquire the largest US satellite-TV provider, sending its 5 year risk premium 29bps tighter from 85.5 to 56.5bps.»
[4] «Overnight 3G Capital Partners and Berkshire Hathaway have offered to merge Kraft Foods Group with HNZ (HJ Heinz Company) to create The Kraft Heinz Co. 3G and Berkshire will own 51% on the new company, while existing Kraft shareholders will get 49% and a $16.5 special dividend. Importantly for the CDS market, no new debt will be issued and the management announced that they are committed to maintaining an investment grade rating going forward. The operation is effectively removing the LBO risk which was overhanging on the food and beverage sector as 3G now appears committed (at least in the medium term). So entire complex traded better today, but the most spectacular move was seen in HNZ’s 5 year CDS which gapped 119bps to 64bps as investors expect it to be a dead box going forward.»
[5] «The credit market closed almost unchanged on most indices, even though the range was 3bps on iTraxx Main and 10bps on iTraxx Crossover. This grapple shows that winners balanced losers and most investment grade names were little changed. The notable exceptions were names in the news: SUEDZU (Suedzucker Mannheim Ochsenfurt AG) spooked investors with a profit warning and slashed EBITDA estimates for 2015 on the back of increasing competition and rising inventories, while BOUY (Bouygues SA) was rewarded for considering the sale of their Telecom unit to Illiad. HOLZSW (Holcim Ltdtion) is still stubbornly refusing to go wider, as Moody’s said the merged entity with Lafarge would be in investment grade territory, forcing more shorts out of their position. »

Question: What were the credit ratings for Conagra and Lamb Weston after the spin-off?

Reasoning: Let's think step by step in order to determine the credit ratings for Conagra and Lamb Weston after the spin-off. From the context, we know that Conagra's credit rating went from BBB- to BBB, while Lamb Weston's credit rating is BB.

Answer: Conagra's credit rating after the spin-off was BBB, and Lamb Weston's credit rating was BB.

---

Context:
[1] «Over the last fortnight, MONTE’s ( Banca Monte dei Paschi  ) stock has roughly doubled. The 5-year risk premium of its senior debt has been cut by 125bps to 350bps since late September. This morning, it looked as if this amazing ride could continue for a while. The company outlined plans to eliminate 2,600 jobs, shut 500 branches and slash the lender's €28Bln pile of bad loans. That sent the stock another 20% up and the debt risk premium another 20bps tighter. To complete its turnaround, the bank also needs to raise €5Bln in fresh capital by year end though, and the CEO admitted that his effort had only begun in that respect. The plan involves a voluntary debt to equity swap, in order to cut the amount MONTE would need to raise from shareholders. The bank wants to spin off €28Bln of souring loan, swap debt for equity and manage a capital increase all at the same time in an exceptionally compressed timeframe. To make things trickier still, there is the December 4 constitutional referendum in Italy which could lead to a increased volatility towards the end of the year. When that reality hit investors, they sent the stock 30% down from its - 15% down on the day  -and the risk premium back to almost unchanged on the day.
 »
[2] «Once the results of the Italian referendum were known, MONTE’s ( Banca Monte dei Paschi  ) board asked to the ECB an extension from the end of the year to Jan. 20 of the deadline set for its recapitalisation exercise “due to the changed reference context”. A delay would have allowed the bank more time to find investors while Italian leaders put a new government in place following the resignation of Prime Minister Matteo Renzi. Early in the afternoon, it emerged that the ECB rejected the bank’s request, increasing the likelihood of a state bailout that would impose losses on shareholders and bondholders. Up to that point, the credit market had been back on the bullish trajectory it has followed since Monday. If the news was not enough to derail the whole market (iTraxx Main closed 1.5bps at 72.5bps and iTraxx Crossover 8bps tighter at 306.5bps), it reversed the trend of iTraxx Financial Senior and Subordinated which closed 1bps wider at 98bps (2bps off the intraday tights) and 3.5bps wider at 225.5bps (12bps off the intraday tights) respectively. On Monday, we might learn how systemically important MONTE really is…»
[3] «Italian banks have been much talked about over the last few sessions. Positive vibes first came during the week-end when Banca Popolare Di Milano and BPIM ( Banca Popolare SC ) won shareholders’ approval for a merger creating Italy’s third largest lender. Today, MONTE’s ( Banca Monte dei Paschi  ) board met after asking their CEO to look into a proposal by Mr Passera, Italy’s ex-minister for economic development and former CEO of Intesa Sanpaolo, to shore up its finances. The plan envisions a €5Bln capital increase, which would include a €1Bln share sale to existing shareholders and €2.5Bln from new long terms backers. While volumes traded on peripheral banks are still dwarfed by transactions on DB, these string of positive news enabled them to outperformed their peers recently and they are the brightest green spots in the above grapple.

Meanwhile, the broader credit market also benefitted from the better tone in the financial sector and from a few earnings beats which buoyed the equity market. Until tomorrow night, iTraxx Main and CDXIG could find it difficult to trade significantly away from their current levels (72.5bps and 75bps respectively) as these are important pins for options expiring late in the afternoon.
»
[4] «MONTE ( Banca Monte dei Paschi  ) gave some details about their debt for equity exchange yesterday night. They plan to offer equity worth between 85% and 100% of face value to the holders of their €4.3Bln outstanding subordinated bonds. The generous terms reflect the need to deliver on this transaction as fully as possible. Indeed, they want to keep the amount of fresh external equity to be raised to a minimum, as the €5Bln total capital increase planned before the end of the year is instrumental in a process that should lead to the deconsolidation of their almost €28Bln portfolio of non-performing loans. Renewed confidence in the success of the deal led investors to send MONTE’s 5-year risk premium 12bps tighter to 392bps, and benefitted the whole Italian complex which outperformed other members of the European financial sector.

Meanwhile, the broader credit market benefitted from the stabilisation of interest rates in Europe and in the US which held onto their overnight (modest) gains throughout the session. iTraxx Crossover and CDX High Yield were the outperformers (closing 9bps and 16bps tighter at 341bps and 418bps respectively), while iTraxx Main (ITXEB) and CDX IG closed 1.5bps and 3bps tighter at 77.5bps and 75bps respectively. Tomorrow is option expiry day on credit indices, and chances are that ITXEB will be attracted by the 75bps strike which represents an important option pin.
»
[5] «On Friday, it emerged that Fortress Investment Group and Elliott Capital Management had dropped out of talks to buy bad loans from MONTE ( Banca Monte dei Paschi  ) complicating the rescue plan for the lender backed by the Italian government. They were the only international bidders for the riskier tranches of MONTE’s bad loan securitization. That leaves Atlante, the fund set up to help the struggling Italian banking sector, as the only potential buyer and jeopardizes the asset sale, which is a key part of the plan to restructure the bank with a capital injection from the state, after MONTE failed to shore up capital privately. Ultimately, it could also make similar rescue plans for two other northern Italian lenders, Veneto Banca Spa and Popolare Vicenza Spa, much more difficult to pull off. Surprisingly, if MONTE’s 5-year risk premium was marked aggressively wider - insuring senior debt now costs 330bps per year, while insuring subordinated costs 73.5% upfront -, it did not feed through the whole Italian banking sector and most names were actually unchanged to a tad tighter.»

Question: What is the intended purpose of the capital increase by Banca Monte dei Paschi?

Reasoning: Let's think step by step in order to[32m determine the intended purpose of the capital increase by Banca Monte dei Paschi. From the context, we know that the capital increase planned by Banca Monte dei Paschi is instrumental in a process that should lead to the deconsolidation of their almost €28 billion portfolio of non-performing loans.

Answer: The intended purpose of the capital increase by Banca Monte dei Paschi is to lead to the deconsolidation of their almost €28 billion portfolio of non-performing loans.[0m

Let’s perform the evaluation of this RAG on the devset:

evaluate(compiled_rag, metric=llm_metric)

  0%|          | 0/10 [00:00<?, ?it/s]

Test Question: What sectors does NXP Semiconductors NV primarily supply chips for?
Predicted Answer: NXP Semiconductors NV primarily supplies chips for the auto industry, including applications like advanced driver assistance systems, infotainment, and in-vehicle networking.


Average Metric: 4.0 / 1  (400.0):  10%|█         | 1/10 [00:00<00:04,  2.23it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What are some of the applications of NXP's chips in the auto industry?
Predicted Answer: Some of the applications of NXP's chips in the auto industry include advanced driver assistance systems, "infotainment," and in-vehicle networking between different car systems.


Average Metric: 8.0 / 2  (400.0):  20%|██        | 2/10 [00:00<00:03,  2.21it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: How did NXP's Q1 2016 earnings report compare to analysts' expectations?
Predicted Answer: NXP's Q1 2016 earnings report was in line with analysts' expectations.


Average Metric: 11.4 / 3  (380.0):  30%|███       | 3/10 [00:01<00:03,  2.25it/s]

Faithful: 5
Detail: 2
Overall: 5
Test Question: What did NXP's management say about the demand and market conditions during their earnings report?
Predicted Answer: NXP's management mentioned that the overall demand continues to be subdued, but they also stated that headwinds experienced last year should begin to subside in the coming quarter.


Average Metric: 15.4 / 4  (385.0):  40%|████      | 4/10 [00:01<00:02,  2.18it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What was NXP's revenue and gross margin outlook for Q2, and how did it compare to the consensus estimates?
Predicted Answer: NXP's revenue outlook for Q2 was $2.3-2.4 billion, slightly ahead of the consensus of $2.3 billion, and the gross margin outlook was 49.5-50.5%, slightly ahead of the consensus of 49.8%.


Average Metric: 19.4 / 5  (388.0):  50%|█████     | 5/10 [00:02<00:02,  2.13it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What steps has Ball taken to win regulatory approval for its acquisition of Rexam?
Predicted Answer: Ball has agreed to sell plants in Brazil and Europe, along with innovation and support functions in several countries, to ARGID (Ardagh Packaging) to satisfy regulatory concerns for its acquisition of Rexam.


Average Metric: 23.4 / 6  (390.0):  60%|██████    | 6/10 [00:02<00:01,  2.05it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: Who is acquiring the assets divested by Ball and Rexam, and what is the significance of this acquisition?
Predicted Answer: ARGID (Ardagh Packaging) is acquiring the assets divested by Ball and Rexam, and this acquisition is significant as it enables ARGID to acquire assets with combined revenues of around $3 billion and will cost the company approximately $3.4 billion.


Average Metric: 27.4 / 7  (391.4):  70%|███████   | 7/10 [00:03<00:01,  2.03it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: Why did ARGID's risk premium not participate in the recent rally of the iTraxx Crossover index?
Predicted Answer: ARGID's risk premium did not participate in the recent rally of the iTraxx Crossover index because investors were skewed towards buying protection on individual names like ARGID, even though the overall index was trading tighter.


Average Metric: 27.4 / 8  (342.5):  80%|████████  | 8/10 [00:03<00:00,  2.07it/s]

Faithful: No
Detail: 5
Overall: 1
Error for example in dev set:          could not convert string to float: 'No'
Test Question: How did the market react to the announcement regarding ARGID's involvement in the Ball-Rexam deal?
Predicted Answer: The market reacted to the announcement regarding ARGID's involvement in the Ball-Rexam deal by marking ARGID's 5-year CDS 36bps wider to 452bps.


Average Metric: 31.4 / 9  (348.9):  90%|█████████ | 9/10 [00:04<00:00,  2.22it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What adjustments did Alcoa Inc make to its global aluminum demand forecast for 2016?
Predicted Answer: Alcoa Inc lowered its forecast for global demand of aluminum in 2016 from a 6% increase to a 5% increase.


Average Metric: 35.4 / 10  (354.0): 100%|██████████| 10/10 [00:07<00:00,  1.40it/s]

Faithful: 5
Detail: 5
Overall: 5
Average Metric: 35.4 / 10  (354.0%)

	question	answer	llm_metric
0	What sectors does NXP Semiconductors NV primarily supply chips for?	NXP Semiconductors NV primarily supplies chips for the auto industry, including applications like advanced driver assistance systems, infotainment, and in-vehicle networking.	4.0
1	What are some of the applications of NXP's chips in the auto industry?	Some of the applications of NXP's chips in the auto industry include advanced driver assistance systems, "infotainment," and in-vehicle networking between different car systems.	4.0
2	How did NXP's Q1 2016 earnings report compare to analysts' expectations?	NXP's Q1 2016 earnings report was in line with analysts' expectations.	3.4
3	What did NXP's management say about the demand and market conditions during their earnings report?	NXP's management mentioned that the overall demand continues to be subdued, but they also stated that headwinds experienced last year should begin to subside in...	4.0
4	What was NXP's revenue and gross margin outlook for Q2, and how did it compare to the consensus estimates?	NXP's revenue outlook for Q2 was $2.3-2.4 billion, slightly ahead of the consensus of $2.3 billion, and the gross margin outlook was 49.5-50.5%, slightly ahead...	4.0

... 5 more rows not displayed ...

354.0

round(354 / 348 , 2), "small metric improvement only..."

(1.02, 'small metric improvement only...')

We can now use the ‘improved’ RAG system (basically, a ‘better’ lengthier prompt containing several examples (hence the name bootstrap few shot):

compiled_rag("Did the OPEC members decide to cut oil production? When? Where? Why?").answer

'Yes, OPEC members decided to cut oil production. The decision was made during a meeting in Vienna, where they agreed to reduce output by 1.2 million barrels per day and cap it at 32.5 million barrels per day.'

Using DSPy to optimize the RAG system with `BayesianSignatureOptimizer`

from dspy.teleprompt import BayesianSignatureOptimizer

llm_prompter = dspy.OpenAI(model='gpt-4', max_tokens=2000, model_type='chat')

teleprompter = BayesianSignatureOptimizer(task_model=dspy.settings.lm,
                                          metric=llm_metric,
                                          prompt_model=llm_prompter,
                                          n=5,
                                          verbose=False)

kwargs = dict(num_threads=1, display_progress=True, display_table=0)
third_compiled_rag = teleprompter.compile(RAG(), devset=devset,
                                         optuna_trials_num=3,
                                         max_bootstrapped_demos=4,
                                         max_labeled_demos=4,
                                         eval_kwargs=kwargs)

[31m[WARNING] BayesianSignatureOptimizer has been deprecated and replaced with MIPRO.  BayesianSignatureOptimizer will be removed in a future release. [31m
[93m[1mWARNING: Projected Language Model (LM) Calls[0m

Please be advised that based on the parameters you have set, the maximum number of LM calls is projected as follows:

[93m- Task Model: [94m[1m10[0m[93m examples in dev set * [94m[1m3[0m[93m trials * [94m[1m# of LM calls in your program[0m[93m = ([94m[1m30 * # of LM calls in your program[0m[93m) task model calls[0m
[93m- Prompt Model: # data summarizer calls (max [94m[1m10[0m[93m) + [94m[1m5[0m[93m * [94m[1m1[0m[93m lm calls in program = [94m[1m15[0m[93m prompt model calls[0m

[93m[1mEstimated Cost Calculation:[0m

[93mTotal Cost = (Number of calls to task model * (Avg Input Token Length per Call * Task Model Price per Input Token + Avg Output Token Length per Call * Task Model Price per Output Token) 
            + (Number of calls to prompt model * (Avg Input Token Length per Call * Task Prompt Price per Input Token + Avg Output Token Length per Call * Prompt Model Price per Output Token).[0m

For a preliminary estimate of potential costs, we recommend you perform your own calculations based on the task
and prompt models you intend to use. If the projected costs exceed your budget or expectations, you may consider:

[93m- Reducing the number of trials (`num_trials`), the size of the trainset, or the number of LM calls in your program.[0m
[93m- Using a cheaper task model to optimize the prompt.[0m


  0%|          | 0/10 [00:00<?, ?it/s]

Test Question: Who is acquiring the assets divested by Ball and Rexam, and what is the significance of this acquisition?
Predicted Answer: ARGID (Ardagh Packaging) is acquiring the assets divested by Ball and Rexam. This acquisition is significant as it will allow ARGID to acquire assets with combined revenues of around $3 billion, costing the company approximately $3.4 billion, as part of a divestment program to satisfy regulatory concerns related to the Ball-Rexam deal.


 10%|█         | 1/10 [00:00<00:03,  2.38it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: How did the market react to the announcement regarding ARGID's involvement in the Ball-Rexam deal?
Predicted Answer: The market reacted to the announcement regarding ARGID's involvement in the Ball-Rexam deal by marking ARGID's 5-year CDS 36bps wider to 452bps.


 20%|██        | 2/10 [00:00<00:03,  2.44it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What adjustments did Alcoa Inc make to its global aluminum demand forecast for 2016?
Predicted Answer: Alcoa Inc adjusted its global aluminum demand forecast for 2016 by lowering it from a 6% increase to a 5% increase and decreasing their market deficit projection from 1.2 million metric tons to 1.1 million metric tons.


 30%|███       | 3/10 [00:03<00:09,  1.40s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: Why did ARGID's risk premium not participate in the recent rally of the iTraxx Crossover index?
Predicted Answer: ARGID's risk premium did not participate in the recent rally of the iTraxx Crossover index because it has been dropped from the index and is no longer included in the group of entities being traded.


 40%|████      | 4/10 [00:03<00:05,  1.01it/s]

Faithful: No
Detail: 5
Overall: 1
Failed to run or to evaluate example Example({'question': "Why did ARGID's risk premium not participate in the recent rally of the iTraxx Crossover index?"}) (input_keys={'question'}) with <function llm_metric at 0x7b1624ec8040> due to could not convert string to float: 'No'.
Test Question: What steps has Ball taken to win regulatory approval for its acquisition of Rexam?
Predicted Answer: Ball has agreed with Rexam to sell plants in Brazil and Europe, along with innovation and support functions in several countries, to ARGID (Ardagh Packaging) to satisfy regulatory concerns. This divestment program is subject to regulatory approvals and will enable ARGID to acquire assets with combined revenues of around $3 billion. Additionally, ARGID will issue $2.85 billion of secured and unsecured notes to help finance the operation.


 50%|█████     | 5/10 [00:04<00:04,  1.21it/s]


Faithful: 5
Detail: 5
Overall: 5
Bootstrapped 4 full traces after 6 examples in round 0.


  0%|          | 0/10 [00:00<?, ?it/s]

Test Question: What steps has Ball taken to win regulatory approval for its acquisition of Rexam?
Predicted Answer: Ball has agreed with Rexam to sell plants in Brazil and Europe, along with innovation and support functions in several countries, to ARGID (Ardagh Packaging) to satisfy regulatory concerns. This divestment program is subject to regulatory approvals and will enable ARGID to acquire assets with combined revenues of around $3 billion. Additionally, ARGID will issue $2.85 billion of secured and unsecured notes to help finance the operation.


 10%|█         | 1/10 [00:00<00:03,  2.74it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What adjustments did Alcoa Inc make to its global aluminum demand forecast for 2016?
Predicted Answer: Alcoa Inc adjusted its global aluminum demand forecast for 2016 by lowering it from a 6% increase to a 5% increase and decreasing their market deficit projection from 1.2 million metric tons to 1.1 million metric tons.


 20%|██        | 2/10 [00:00<00:02,  2.69it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What did NXP's management say about the demand and market conditions during their earnings report?
Predicted Answer: The management of NXP stated that overall demand continues to be subdued, but they also mentioned that the headwinds experienced in the previous year should begin to subside in the coming quarter.


 30%|███       | 3/10 [00:01<00:02,  2.49it/s]

Faithful: 5
Detail: 5
Overall: 5


 40%|████      | 4/10 [00:01<00:02,  2.56it/s]


Test Question: What was NXP's revenue and gross margin outlook for Q2, and how did it compare to the consensus estimates?
Predicted Answer: The revenue outlook for Q2 was $2.3-2.4 billion, slightly above the consensus of $2.3 billion, and the gross margin outlook was 49.5-50.5%, slightly above the consensus of 49.8%.
Faithful: 5
Detail: 5
Overall: 5
Bootstrapped 4 full traces after 5 examples in round 0.


 10%|█         | 1/10 [00:00<00:02,  3.10it/s]

Test Question: What are some of the applications of NXP's chips in the auto industry?
Predicted Answer: Some of the applications of NXP's chips in the auto industry include advanced driver assistance systems, "infotainment," and in-vehicle networking between different car systems.
Faithful: 5
Detail: 5
Overall: 5
Test Question: What steps has Ball taken to win regulatory approval for its acquisition of Rexam?
Predicted Answer: Ball has agreed with Rexam to sell plants in Brazil and Europe, along with innovation and support functions in several countries, to ARGID (Ardagh Packaging) to satisfy regulatory concerns. This divestment program is subject to regulatory approvals and will enable ARGID to acquire assets with combined revenues of around $3 billion. Additionally, ARGID will issue $2.85 billion of secured and unsecured notes to help finance the operation.
Faithful: 5
Detail: 5


 20%|██        | 2/10 [00:00<00:02,  2.80it/s]

Overall: 5


 30%|███       | 3/10 [00:01<00:02,  2.73it/s]

Test Question: Who is acquiring the assets divested by Ball and Rexam, and what is the significance of this acquisition?
Predicted Answer: ARGID (Ardagh Packaging) is acquiring the assets divested by Ball and Rexam. This acquisition is significant as it will allow ARGID to acquire assets with combined revenues of around $3 billion, costing the company approximately $3.4 billion, as part of a divestment program to satisfy regulatory concerns related to the Ball-Rexam deal.
Faithful: 5
Detail: 5
Overall: 5


 40%|████      | 4/10 [00:01<00:02,  2.81it/s]


Test Question: What sectors does NXP Semiconductors NV primarily supply chips for?
Predicted Answer: NXP Semiconductors NV primarily supplies chips for the smartphone and auto industries.
Faithful: 5
Detail: 2
Overall: 5
Bootstrapped 4 full traces after 5 examples in round 0.


  0%|          | 0/10 [00:00<?, ?it/s]

Test Question: How did the market react to the announcement regarding ARGID's involvement in the Ball-Rexam deal?
Predicted Answer: The market reacted to the announcement regarding ARGID's involvement in the Ball-Rexam deal by marking ARGID's 5-year CDS 36bps wider to 452bps.


 10%|█         | 1/10 [00:00<00:03,  2.49it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: How did NXP's Q1 2016 earnings report compare to analysts' expectations?
Predicted Answer: NXP's Q1 2016 earnings report was in line with analysts' expectations, and the company managed to surprise investors with their numbers.


 20%|██        | 2/10 [00:00<00:02,  2.67it/s]

Faithful: 5
Detail: 3
Overall: 5
Test Question: What sectors does NXP Semiconductors NV primarily supply chips for?
Predicted Answer: NXP Semiconductors NV primarily supplies chips for the smartphone and auto industries.


 30%|███       | 3/10 [00:01<00:02,  2.60it/s]

Faithful: 5
Detail: 2
Overall: 5


 40%|████      | 4/10 [00:01<00:02,  2.63it/s]

Test Question: Why did ARGID's risk premium not participate in the recent rally of the iTraxx Crossover index?
Predicted Answer: ARGID's risk premium did not participate in the recent rally of the iTraxx Crossover index because it has been dropped from the index and is no longer included in the group of entities being traded.
Faithful: No
Detail: 5
Overall: 1
Failed to run or to evaluate example Example({'question': "Why did ARGID's risk premium not participate in the recent rally of the iTraxx Crossover index?"}) (input_keys={'question'}) with <function llm_metric at 0x7b1624ec8040> due to could not convert string to float: 'No'.


 50%|█████     | 5/10 [00:01<00:01,  2.64it/s]

Test Question: Who is acquiring the assets divested by Ball and Rexam, and what is the significance of this acquisition?
Predicted Answer: ARGID (Ardagh Packaging) is acquiring the assets divested by Ball and Rexam. This acquisition is significant as it will allow ARGID to acquire assets with combined revenues of around $3 billion, costing the company approximately $3.4 billion, as part of a divestment program to satisfy regulatory concerns related to the Ball-Rexam deal.
Faithful: 5
Detail: 5
Overall: 5
Bootstrapped 4 full traces after 6 examples in round 0.



[I 2024-04-14 12:10:27,870] A new study created in memory with name: no-name-3c72ec8f-9362-499c-9f01-4d2ed8528ed8


Starting trial #0


  0%|          | 0/10 [00:00<?, ?it/s]

Test Question: What sectors does NXP Semiconductors NV primarily supply chips for?
Predicted Answer: NXP Semiconductors NV primarily supplies chips for the auto industry, specifically for applications in advanced driver assistance systems, infotainment, and in-vehicle networking between different car systems.


Average Metric: 4.0 / 1  (400.0):  10%|█         | 1/10 [00:00<00:02,  3.03it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What are some of the applications of NXP's chips in the auto industry?
Predicted Answer: NXP's chips in the auto industry have applications ranging from advanced driver assistance systems to "infotainment" and in-vehicle networking between different car systems.


Average Metric: 8.0 / 2  (400.0):  20%|██        | 2/10 [00:00<00:02,  3.07it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: How did NXP's Q1 2016 earnings report compare to analysts' expectations?
Predicted Answer: NXP's Q1 2016 earnings report was in line with analysts' expectations. The company surprised investors with numbers that met the consensus. Additionally, their Q2 outlook slightly exceeded consensus estimates, with revenues expected to be $2.3-2.4 billion compared to a consensus of $2.3 billion and a gross margin of 49.5-50.5% versus a consensus of 49.8%.


Average Metric: 12.0 / 3  (400.0):  30%|███       | 3/10 [00:01<00:02,  2.82it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What did NXP's management say about the demand and market conditions during their earnings report?
Predicted Answer: NXP's management mentioned that overall demand continues to be subdued, but they also stated that the headwinds experienced last year should begin to subside in the coming quarter. This cautious optimism led them to present a Q2 outlook that slightly exceeded consensus estimates for revenues and gross margin.


Average Metric: 16.0 / 4  (400.0):  40%|████      | 4/10 [00:01<00:02,  2.82it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What was NXP's revenue and gross margin outlook for Q2, and how did it compare to the consensus estimates?
Predicted Answer: The revenue outlook for Q2 was $2.3-2.4 billion, slightly above the consensus of $2.3 billion, and the gross margin outlook was 49.5-50.5%, slightly above the consensus of 49.8%.


Average Metric: 20.0 / 5  (400.0):  50%|█████     | 5/10 [00:01<00:01,  2.87it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What steps has Ball taken to win regulatory approval for its acquisition of Rexam?
Predicted Answer: Ball has agreed with Rexam to sell plants in Brazil and Europe, along with innovation and support functions in several countries, to ARGID (Ardagh Packaging) to satisfy regulatory concerns. This divestment program is subject to regulatory approvals and will enable ARGID to acquire assets with combined revenues of around $3 billion. Additionally, ARGID will issue $2.85 billion of secured and unsecured notes to help finance the operation.


Average Metric: 24.0 / 6  (400.0):  50%|█████     | 5/10 [00:02<00:01,  2.87it/s]

Faithful: 5
Detail: 5
Overall: 5


Average Metric: 28.0 / 7  (400.0):  70%|███████   | 7/10 [00:02<00:01,  2.73it/s]

Test Question: Who is acquiring the assets divested by Ball and Rexam, and what is the significance of this acquisition?
Predicted Answer: The assets divested by Ball and Rexam are being acquired by ARGID (Ardagh Packaging). This acquisition is significant as it allows ARGID to acquire assets with combined revenues of around $3 billion, costing the company approximately $3.4 billion, and will be financed through the issuance of $2.85 billion of secured and unsecured notes.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 29.6 / 8  (370.0):  80%|████████  | 8/10 [00:02<00:00,  2.71it/s]

Test Question: Why did ARGID's risk premium not participate in the recent rally of the iTraxx Crossover index?
Predicted Answer: ARGID's risk premium did not participate in the recent rally of the iTraxx Crossover index because they were rumored to be the leading bidders for Ball-Rexam assets, which led investors to mark their 5-year CDS wider. Additionally, the perspective of more leverage due to the acquisition of assets from Ball-Rexam also contributed to investors widening ARGID's risk premium.
Faithful: 1
Detail: 5
Overall: 1
Test Question: How did the market react to the announcement regarding ARGID's involvement in the Ball-Rexam deal?
Predicted Answer: The market reacted to the announcement of ARGID's involvement in the Ball-Rexam deal by widening ARGID's 5-year CDS risk premium by 36bps to 452bps.


Average Metric: 33.6 / 9  (373.3):  90%|█████████ | 9/10 [00:03<00:00,  2.63it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What adjustments did Alcoa Inc make to its global aluminum demand forecast for 2016?
Predicted Answer: Alcoa Inc adjusted its global aluminum demand forecast for 2016 by lowering it from a projected 6% increase to a 5% increase. Additionally, they decreased their market deficit projection from 1.2 million metric tons to 1.1 million metric tons due to slowing Chinese demand.


Average Metric: 37.6 / 10  (376.0): 100%|██████████| 10/10 [00:03<00:00,  2.71it/s]
[I 2024-04-14 12:10:31,595] Trial 0 finished with value: 376.0 and parameters: {'135334835491440_predictor_instruction': 1, '135334835491440_predictor_demos': 2}. Best is trial 0 with value: 376.0.


Faithful: 5
Detail: 5
Overall: 5
Average Metric: 37.6 / 10  (376.0%)
Starting trial #1


  0%|          | 0/10 [00:00<?, ?it/s]

Test Question: What sectors does NXP Semiconductors NV primarily supply chips for?
Predicted Answer: NXP Semiconductors NV primarily supplies chips for the auto industry, specifically for applications in advanced driver assistance systems, infotainment, and in-vehicle networking between different car systems.


Average Metric: 4.0 / 1  (400.0):  10%|█         | 1/10 [00:00<00:03,  2.40it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What are some of the applications of NXP's chips in the auto industry?
Predicted Answer: NXP's chips in the auto industry have applications ranging from advanced driver assistance systems to "infotainment" and in-vehicle networking between different car systems.


Average Metric: 8.0 / 2  (400.0):  20%|██        | 2/10 [00:00<00:03,  2.55it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: How did NXP's Q1 2016 earnings report compare to analysts' expectations?
Predicted Answer: NXP's Q1 2016 earnings report was in line with analysts' expectations. The company surprised investors with numbers that met the consensus. Additionally, their Q2 outlook slightly exceeded consensus estimates, with revenues expected to be $2.3-2.4 billion compared to a consensus of $2.3 billion and a gross margin of 49.5-50.5% versus a consensus of 49.8%.


Average Metric: 12.0 / 3  (400.0):  30%|███       | 3/10 [00:01<00:02,  2.64it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What did NXP's management say about the demand and market conditions during their earnings report?
Predicted Answer: NXP's management mentioned that overall demand continues to be subdued, but they also stated that the headwinds experienced last year should begin to subside in the coming quarter. This cautious optimism led them to present a Q2 outlook that slightly exceeded consensus estimates for revenues and gross margin.


Average Metric: 16.0 / 4  (400.0):  40%|████      | 4/10 [00:01<00:02,  2.62it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What was NXP's revenue and gross margin outlook for Q2, and how did it compare to the consensus estimates?
Predicted Answer: The revenue outlook for Q2 was $2.3-2.4 billion, slightly above the consensus of $2.3 billion, and the gross margin outlook was 49.5-50.5%, slightly above the consensus of 49.8%.


Average Metric: 20.0 / 5  (400.0):  50%|█████     | 5/10 [00:01<00:01,  2.68it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What steps has Ball taken to win regulatory approval for its acquisition of Rexam?
Predicted Answer: Ball has agreed with Rexam to sell plants in Brazil and Europe, along with innovation and support functions in several countries, to ARGID (Ardagh Packaging) to satisfy regulatory concerns. This divestment program is subject to regulatory approvals and will enable ARGID to acquire assets with combined revenues of around $3 billion. Additionally, ARGID will issue $2.85 billion of secured and unsecured notes to help finance the operation.


Average Metric: 24.0 / 6  (400.0):  60%|██████    | 6/10 [00:02<00:01,  2.74it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: Who is acquiring the assets divested by Ball and Rexam, and what is the significance of this acquisition?
Predicted Answer: The assets divested by Ball and Rexam are being acquired by ARGID (Ardagh Packaging). This acquisition is significant as it allows ARGID to acquire assets with combined revenues of around $3 billion, costing the company approximately $3.4 billion, and will be financed through the issuance of $2.85 billion of secured and unsecured notes.


Average Metric: 28.0 / 7  (400.0):  70%|███████   | 7/10 [00:02<00:01,  2.67it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: Why did ARGID's risk premium not participate in the recent rally of the iTraxx Crossover index?
Predicted Answer: ARGID's risk premium did not participate in the recent rally of the iTraxx Crossover index because they were rumored to be the leading bidders for Ball-Rexam assets, which led investors to mark their 5-year CDS wider. Additionally, the perspective of more leverage due to the acquisition of assets from Ball-Rexam also contributed to investors widening ARGID's risk premium.


Average Metric: 29.6 / 8  (370.0):  80%|████████  | 8/10 [00:03<00:00,  2.68it/s]

Faithful: 1
Detail: 5
Overall: 1
Test Question: How did the market react to the announcement regarding ARGID's involvement in the Ball-Rexam deal?
Predicted Answer: The market reacted to the announcement of ARGID's involvement in the Ball-Rexam deal by widening ARGID's 5-year CDS risk premium by 36bps to 452bps.


Average Metric: 33.6 / 9  (373.3):  90%|█████████ | 9/10 [00:03<00:00,  2.74it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What adjustments did Alcoa Inc make to its global aluminum demand forecast for 2016?
Predicted Answer: Alcoa Inc adjusted its global aluminum demand forecast for 2016 by lowering it from a projected 6% increase to a 5% increase. Additionally, they decreased their market deficit projection from 1.2 million metric tons to 1.1 million metric tons due to slowing Chinese demand.


Average Metric: 37.6 / 10  (376.0): 100%|██████████| 10/10 [00:03<00:00,  2.65it/s]
[I 2024-04-14 12:10:35,414] Trial 1 finished with value: 376.0 and parameters: {'135334835491440_predictor_instruction': 1, '135334835491440_predictor_demos': 2}. Best is trial 0 with value: 376.0.


Faithful: 5
Detail: 5
Overall: 5
Average Metric: 37.6 / 10  (376.0%)
Starting trial #2


Average Metric: 3.4 / 1  (340.0):   0%|          | 0/10 [00:00<?, ?it/s]

Test Question: What sectors does NXP Semiconductors NV primarily supply chips for?
Predicted Answer: NXP Semiconductors NV primarily supplies chips for the smartphone and auto industries.
Faithful: 5
Detail: 2
Overall: 5


Average Metric: 7.4 / 2  (370.0):  20%|██        | 2/10 [00:00<00:03,  2.64it/s]

Test Question: What are some of the applications of NXP's chips in the auto industry?
Predicted Answer: Some of the applications of NXP's chips in the auto industry include advanced driver assistance systems, "infotainment," and in-vehicle networking between different car systems.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 11.0 / 3  (366.7):  30%|███       | 3/10 [00:01<00:02,  2.72it/s]

Test Question: How did NXP's Q1 2016 earnings report compare to analysts' expectations?
Predicted Answer: NXP's Q1 2016 earnings report was in line with analysts' expectations, and the company managed to surprise investors with their numbers.
Faithful: 5
Detail: 3
Overall: 5


Average Metric: 15.0 / 4  (375.0):  30%|███       | 3/10 [00:01<00:02,  2.72it/s]

Test Question: What did NXP's management say about the demand and market conditions during their earnings report?
Predicted Answer: The management of NXP stated that overall demand continues to be subdued, but they also mentioned that the headwinds experienced in the previous year should begin to subside in the coming quarter.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 19.0 / 5  (380.0):  50%|█████     | 5/10 [00:01<00:01,  2.77it/s]

Test Question: What was NXP's revenue and gross margin outlook for Q2, and how did it compare to the consensus estimates?
Predicted Answer: The revenue outlook for Q2 was $2.3-2.4 billion, slightly above the consensus of $2.3 billion, and the gross margin outlook was 49.5-50.5%, slightly above the consensus of 49.8%.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 23.0 / 6  (383.3):  60%|██████    | 6/10 [00:02<00:01,  2.71it/s]

Test Question: What steps has Ball taken to win regulatory approval for its acquisition of Rexam?
Predicted Answer: Ball has agreed with Rexam to sell plants in Brazil and Europe, along with innovation and support functions in several countries, to ARGID (Ardagh Packaging) to satisfy regulatory concerns. This divestment program is subject to regulatory approvals and will enable ARGID to acquire assets with combined revenues of around $3 billion. Additionally, ARGID will issue $2.85 billion of secured and unsecured notes to help finance the operation.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 27.0 / 7  (385.7):  70%|███████   | 7/10 [00:02<00:01,  2.62it/s]

Test Question: Who is acquiring the assets divested by Ball and Rexam, and what is the significance of this acquisition?
Predicted Answer: ARGID (Ardagh Packaging) is acquiring the assets divested by Ball and Rexam. This acquisition is significant as it will allow ARGID to acquire assets with combined revenues of around $3 billion, costing the company approximately $3.4 billion, as part of a divestment program to satisfy regulatory concerns related to the Ball-Rexam deal.
Faithful: 5
Detail: 5
Overall: 5
Test Question: Why did ARGID's risk premium not participate in the recent rally of the iTraxx Crossover index?
Predicted Answer: ARGID's risk premium did not participate in the recent rally of the iTraxx Crossover index because it has been dropped from the index and is no longer included in the group of entities being traded.


Average Metric: 27.0 / 8  (337.5):  80%|████████  | 8/10 [00:02<00:00,  2.64it/s]

Faithful: No
Detail: 5
Overall: 1
Error for example in dev set:          could not convert string to float: 'No'
Test Question: How did the market react to the announcement regarding ARGID's involvement in the Ball-Rexam deal?
Predicted Answer: The market reacted to the announcement regarding ARGID's involvement in the Ball-Rexam deal by marking ARGID's 5-year CDS 36bps wider to 452bps.


Average Metric: 31.0 / 9  (344.4):  90%|█████████ | 9/10 [00:03<00:00,  2.63it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What adjustments did Alcoa Inc make to its global aluminum demand forecast for 2016?
Predicted Answer: Alcoa Inc adjusted its global aluminum demand forecast for 2016 by lowering it from a 6% increase to a 5% increase and decreasing their market deficit projection from 1.2 million metric tons to 1.1 million metric tons.


Average Metric: 35.0 / 10  (350.0): 100%|██████████| 10/10 [00:03<00:00,  2.69it/s]
[I 2024-04-14 12:10:39,175] Trial 2 finished with value: 350.0 and parameters: {'135334835491440_predictor_instruction': 0, '135334835491440_predictor_demos': 0}. Best is trial 0 with value: 376.0.


Faithful: 5
Detail: 5
Overall: 5
Average Metric: 35.0 / 10  (350.0%)
Returning generate_answer = ChainOfThought(GenerateAnswer(context, question -> answer
    instructions='Answer questions based on the context.'
    context = Field(annotation=str required=True json_schema_extra={'desc': 'may contain relevant facts', '__dspy_field_type': 'input', 'prefix': 'Context:'})
    question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})
    answer = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'output', 'prefix': 'Answer:', 'desc': '${answer}'})
)) from continue_program

third_compiled_rag("How did FirstEnergy's cash reserves stand at the end of 2019, and what was the trend over the previous five years?").answer

"The context does not provide information about FirstEnergy's cash reserves at the end of 2019 or the trend over the previous five years."

We can observe that the ‘meta’ prompt of the RAG has been re-written from the generic Answer questions based on the context. to the more precise Based on the provided business and financial contexts, provide detailed and specific answers that fully address the posed questions, drawing explicitly from the information given.:

dspy.settings.lm.inspect_history(n=1)

Based on the provided business and financial contexts, provide detailed and specific answers that fully address the posed questions, drawing explicitly from the information given.

---

Follow the following format.

Context: may contain relevant facts

Question: ${question}

Reasoning: Let's think step by step in order to ${produce the answer}. We ...

Answer: ${answer}

---

Context:
[1] «In February 2015, Ball agreed to acquire Rexam in a deal that would combine two of the world’s largest makers of cans for drinks, and it has been trying since to win regulatory approval for the deal. Today, it announced that it had agreed with Rexam to sell plants in Brazil and Europe together with innovation and support functions in Brazil, Britain, Germany, Switzerland and the United States to ARGID (Ardagh Packaging) to satisfy some of these regulatory concerns. This divestment program itself is subject to regulatory approvals, but it should enable ARGID to acquire assets with combined revenues of around $3bln and will cost the company approximately $3.4bln. To help finance the operation, ARGID will issue $2.85bln of secured and unsecured notes in the coming weeks. They had been rumoured to be the leading bidders for Ball-Rexam assets for a while, and that is the reason why the risk premium of ARGID never really participated in the rally that took iTraxx Crossover from 336bps on the April 8th to 294bps last Friday. Nevertheless, the quasi-certain perspective of more leverage led investors to mark ARGID’s 5 year CDS 36bps wider (to 452bps) today.»
[2] «This morning, BATSLN ( British American Tobacco Plc ) offered to merge with RAI ( Reynolds American, Inc. ). BATSLN already owns 42% of RAI, and the offer targets the remaining 58%. Over the past few years, several failed attempts were made to take full ownership with a view to create a geographically diverse group, since RAI is focused on the US and BATSLN is weighted towards everywhere else. There would also be scope for scale economies in R&D; for smokeless technologies, justifying the high multiples of the transaction – 16.3 times 12-month trailing Ebitda -. The current proposal values RAI at $56.5/share – that is a 20% premium to yesterday’s close – of which $24.13 will be cash, but discussions have not begun between the two companies and RAI was said to want a higher premium. In any case, the total cash contribution should be in excess of $20Bln and investors are expecting a heavy bond issuance to finance the deal which will increase leverage by roughly one time. They send BATSLN’s 5-year risk premium 7bps wider at 65bps without waiting for the final details of the operation.

Meanwhile, the broader credit market was quiet, and traded volumes on credit indices stood 30 to 40% below average. While risk premia were a touch wider across the board, moves were kept minimal.
»
[3] «According to various press reports, Altria, which controls 27% of SABLN ( SAB Miller Plc ), recently pulled out of a conference appearance to follow up on a similar action by ABIBB ( Anheuser Busch Inbev SA ). People were quick to think something was brewing and today ABIBB confirmed it had made an approach to SABLN’s board. This acquisition would be the biggest in the industry history and would cap more than a decade of consolidation across brewing companies, as consolidation has been used as a way to stave off a slowdown in established markets such as Europe and the US. Even though a deal is currently much more affordable because the price action of SABLN’s has been weak over the last 6 months, the deal would likely cost ABIBB more than $100bln. If both the stocks of both company reacted positively, that is a big number and investors pushed ABIBB’s 5 year risk premium 11bps wider to 79bps, the most in almost 3 years.»
[4] «Today, RAI (Reynolds American, Inc) and Lorillard Inc. announced that they are proceeding with their merger, in a $27.4bln deal that will see RAI become a bigger competitor to U.S. market leader MO (Altria Group Inc). In line with recent press speculation, IMTLN (Imperial Tobacco Group Plc) will acquire $7.1bln worth of assets from the merged entity. In the process, IMTLN will lever up to a level that is not compatible with an IG rating, and the market has recently punished them for that, putting today the final touch to a 20bps widening in a little less than 10 days. But the company has clearly discussed the transaction with the rating agencies before the announcement, and Moody’s was able to reaffirm their rating very quickly, whilst putting them on negative outlook. That last bit is there to make sure that they use some of their ridiculous cash generation towards deleveraging and that they respect a strict timescale to do so. With that in mind, most of the damage to the 5 year risk premium of IMTLN might already be behind us.»
[5] «Yesterday ABIBB ( Anheuser Busch Inbev SA ) made a formal $107bln offer to buy SABLN ( SABMiller plc ), sealing a long anticipated deal that combine the world’s biggest brewers to form a company that will have the number one or two position in 24 of the world’s 30 biggest beer markets according to analysts. To back the acquisition, ABIBB said it obtained the biggest corporate loan on record, $75bln. In the future part of the loans will be replaced by bonds, and the brewer is said to be planning to sell $55bln of bonds across multiple currency and maturities. When it happens, this sale will also be a record. It will exceed the $49bln of bonds that VZ ( Verizon Communications Inc ) issued two years ago to fund its buyout of VOD’s ( Vodafone Group plc ) stake in a wireless venture. But while in September 2013, VZ’s 5 year risk premium jumped 30bps from 70 to 100bps when the jumbo deal was announced, ABIBB’s 5 year risk premium hardly moved at all. It widened today by 2bps to 81bps, which is merely in line with the rest of the market ( iTraxx Main, the barometer of European investment grade credit, widened 3bps to 73.5bps).»

Question: What steps has Ball taken to win regulatory approval for its acquisition of Rexam?

Reasoning: Let's think step by step in order to produce the answer. We need to look at the context provided to understand the actions Ball has taken to win regulatory approval for its acquisition of Rexam.

Answer: Ball has agreed with Rexam to sell plants in Brazil and Europe, along with innovation and support functions in several countries, to ARGID (Ardagh Packaging) to satisfy regulatory concerns. This divestment program is subject to regulatory approvals and will enable ARGID to acquire assets with combined revenues of around $3 billion. Additionally, ARGID will issue $2.85 billion of secured and unsecured notes to help finance the operation.

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Context:
[1] «Yesterday saw the earnings reporting season ‘s starting whistle for Q1 blown by AA ( Alcoa Inc ). They got things started after the closing bell, and the numbers were fairly mixed. While earnings came in above consensus, they missed on revenues. They also lowered their forecast for global demand of aluminium in 2016 – they expect it to climb 5% whereas previously their projection was a 6% increase – and they decreased their market deficit projection – they estimate a 1.1 million metric tons deficit in 2016 versus 1.2 million 3 months ago – as Chinese demand slows. That made AA one of the few names that saw their 5 year risk premium widen (+10bps at 405bps) in an otherwise constructive session. Even if it currently stands far off its February highs of 44%, it means the market assigns a 31% probability to an AA default over during the next 5 years.»
[2] «Like every other quarter, AA ( Alcoa Inc ) will be among the first to report at the very beginning of the second week of October. But while the results might get any scrutiny and generate some volatility as commodity related names have been under the spotlight recently, AA already proved over the last few days that it is not a credit for the faint hearted. On Monday, the company announced its plan to break itself in two by separating its manufacturing operations (the “Value-Add” business still to be named) from the legacy smelting and refining business (which will retain the Alcoa name) that is struggling to overcome booming production from China. Investors were unsure where the debt might end up, and they feared that the bonds might go to the less attractive part of the business, i.e. Alcoa. Accordingly, they sent AA’s 5 year risk premium 140bps wider at 457bps, meaning that a 5 year bond lost roughly 7cts on the dollar on that day. The company management issued a statement late yesterday saying that “Alcoa’s current intent is that the debt of Alcoa would be retained by the Value-Add company for which Alcoa is targeting an investment grade rating”. Guess what? AA’s 5 year risk premium closed at 335bps tonight.»
[3] «We already had a trickle of results in the past few weeks, but the reporting season begun in earnest today. Over the next few weeks, all major companies will be releasing third numbers on both sides of the Atlantic. As is now customary, AA ( Alcoa Inc ) kicked off the proceedings, and they did it in style. The 128-year-old company reported their last earnings before splitting into two entities - the newly branded Arconic which will service aerospace and automotive industries, and the new Alcoa which will focus on their mining and smelting operations in bauxite, alumina and aluminium products – and missed both earnings (32cts/share vs 34cts expected) and revenue (total sales fell to $5.2Bln vs $5.33Bln expected) estimates. More importantly, they lowered Q4 and year-end guidance, adding that next year’s outlook was “very hard” to read. Investors punished the stock and marked it 10% lower, while they sent AA’s 5-year risk premium 17.5bps wider at 250bps, still a long way away from the 650bps it reached at the very beginning of the year though.

Meanwhile, the broader credit market was on the back foot. Credit indices suffered as US equities felt the brunt of a strong dollar and the disappointing start to the earning season. In Europe iTraxx Main and iTraxx Crossover closed 1bp and 4.5bps  wider at 74.5bps and 335bps respectively, while, in the US, CDXIG was 1bp wider at 75bps and CDXHY 2bps wider at 404bps.
»
[4] «The reporting season is only beginning, and this used to be the time of the year when Alcoa was systematically mentioned as it used to be the first corporate to announce its results. Part of the company, which produces and distributes aerospace products, split from Alcoa Corporation, which produces and sells bauxite, alumina and aluminium products, and is now known as ARNC ( Arconic Inc ). It will report in a couple of weeks. Nevertheless, it attracted most of the headlines in the US credit market today. As of Friday, its stock was down 44% from the high it reached mid-January. It had suffered from tensions concerning the US aluminium and steel tariffs that have roiled metal producers. It is in this context that the Wall Street Journal reported late on Friday that private equity firms would be circling the company. Even though it is facing operational challenges, its weak cash generation and attractive valuation offer potential buyers a favourable entry point according to analysts. LBOs are synonymous of increased leverage, and while they sent ARNC’s stock 10% higher, investors pushed the company’s 5-year risk premium 85bps wider at 283bps.»
[5] «In August, ARNC ( Arconic Inc ) announced it was considering selling its building and construction systems unit which makes facades, windows and framing products. On Tuesday, when it reported better than expected quarterly profit and raised its full-year earnings forecast, it said the sale process was under way and had drawn robust interest. Under such circumstances, one would probably expect ARNC’s risk premium to tighten. But during the same earning meeting, the management also announced that the scope of the strategy and portfolio review initiated in February was being extended and its scope broadened “to address additional scenarios”. That gave weight to rumours that private equity firms are circling the company, with Apollo Global Management supposedly about to make an offer in excess of $11bln for the aluminium products maker. With LBO risks gathering and leverage potentially about to be propelled higher, investors in fact pushed ARNC’s 5-year risk premium above 400bps for the first time since 2016. »

Question: What adjustments did Alcoa Inc make to its global aluminum demand forecast for 2016?

Reasoning: Let's think step by step in order to produce the answer. We know from the context that Alcoa Inc adjusted its global aluminum demand forecast for 2016. They initially projected a 6% increase in demand but later revised it to a 5% increase. Additionally, they decreased their market deficit projection from 1.2 million metric tons to 1.1 million metric tons due to slowing Chinese demand.

Answer: Alcoa Inc adjusted its global aluminum demand forecast for 2016 by lowering it from a 6% increase to a 5% increase and decreasing their market deficit projection from 1.2 million metric tons to 1.1 million metric tons.

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Context:
[1] «NXP ( NXP Semiconductors NV ) is a leading chip supplier for smartphones, but it is also a major supplier of chips for the auto industry following its recent acquisition of FSL (Freescale Semiconductor). These chips have a wide range of applications, from advanced driver assistance systems to “infotainment” or in-vehicle networking between different car systems. NXP’s strength in the auto industry is one of the reasons the company managed to surprise investors when it reported Q1 2016 numbers this morning, which were in line with analysts’ expectations. More importantly, even though management said the overall demand continues to be subdued, they also said that headwinds experienced last year should begin to subside in the coming quarter. This cautious optimism enabled them to present a Q2 outlook that came a touch ahead of consensus (revenues of $2.3-2.4bln vs a consensus of $2.3bln; gross margin at 49.5-50.5% vs a consensus of 49.8%). In an otherwise directionless session, NXP was rewarded with a 14bps tightening of its 5 year risk premium at 179bps.»
[2] «The semi-conductor industry has been all over the news since the beginning of the week. After STM ( STMicorelectronics NV ) bidding for Fairchild on Tuesday, NXP ( NXP Semiconductors NV ) grabbed the headlines today, as they reported Q3 earnings. Although they announced revenue just below and EPS above the street consensus, people focused – as one would actually expect them to do - on the guidance for the coming quarters. The company surprised the market by lowering their forecast for fourth quarter revenues (which they expect to be down “low to upper teens range” quarter on quarter), as customers pulled back on orders amid a slowing global economy and higher inventories of unsold chips. The surprise did not come from the direction of the revision, but rather by its magnitude. The stock was first to react and was marked down almost 20%. The 5 year CDS was slower and only moved 12bps wider to 165bps. There is still time to reload shorts on a name that used to belong in the 200/250bps range.»
[3] «Most of the names traded sideways in the credit market today. Given the tight level of the risk premia, that can only be described as undeniable strength. One rare wobble came from NXP (NXP Semiconductors NV). The company announced overnight that it is buying Freescale for $11.8Bln, in a combination of cash and stocks. It aims to expand its market share in chips for cars, surfing the trend pointing to vehicle that drive themselves. The deal will be funded with $1bln and $1bln in new debt, which means an increase in net leverage from 1.7x to 3.5x. The initial reaction was to send the 5 year CDS 20bps wider. But there has been a lot of short risk positions built recently in the name in anticipation of such an operation, making it one of only a handful of names trading hardly tighter since the beginning of the year. Now that the deal turning two challengers into a giant has materialised, this CDS felt pretty squeezy and it closed only marginally wider at 149bps (it is wider as what some see on their screen as all the risk premia in DataGrapple are computed using a 100bps running coupon, and not the 500bps which is standard on NXP).»
[4] «Even though the momentum appears to slow a little bit in the CDS market, all indices had another positive session (with the exception of iTraxx Financial Senior which was 0.25bps wider…). The same was true for the vast majority of names, which saw their risk premia decrease across the board, and selling protection was a tough order today. One exception was NXPBV ( NXP Smeiconductors NV ). Its 5 year CDS was wider (up 2bps at 148bps), and the bid was relatively deep on the back on their $1bln convertible bond issue. The proceeds will be used to purchase stocks and repay existing loans. But if you take a few steps back and look at the 5 year CDS run over the last few years, you will see that the timing of the company is not that bad. The risk premium of NXP has never been that tight. The stock is also trading at its all-time high on the NASDAQ, and interest rates have never been that low. All in all, it is fair to say that the issue of convertible notes probably make sense… for them.»
[5] «Earlier in the year, the control of XRX ( Xerox Corp ) was handed to activist investors Icahn and Deason, after its planned merger with Fuji was scrapped. The company delivered today its first set of results under their guidance, and they were consistent with granting top priority to shareholder returns. The US photocopier which is facing a long running decline in its core business reported net profit that roughly halved and said revenue fell 5.8% year on year to $2.35Bln in the third quarter, below an average analyst estimate of $2.42Bln. Despite that fall, the new management increased its share-repurchase expectations by $200m, even though according to the different rating agencies, XRX does not have any further flexibility within its current BBB rating category. Investors certainly copied that, and sent XRX’s 5-year risk premium 27bps at 224bps.»

Question: What did NXP's management say about the demand and market conditions during their earnings report?

Reasoning: Let's think step by step in order to produce the answer. We need to look at the information provided in the context regarding NXP's earnings report.

Answer: The management of NXP stated that overall demand continues to be subdued, but they also mentioned that the headwinds experienced in the previous year should begin to subside in the coming quarter.

---

Context:
[1] «NXP ( NXP Semiconductors NV ) is a leading chip supplier for smartphones, but it is also a major supplier of chips for the auto industry following its recent acquisition of FSL (Freescale Semiconductor). These chips have a wide range of applications, from advanced driver assistance systems to “infotainment” or in-vehicle networking between different car systems. NXP’s strength in the auto industry is one of the reasons the company managed to surprise investors when it reported Q1 2016 numbers this morning, which were in line with analysts’ expectations. More importantly, even though management said the overall demand continues to be subdued, they also said that headwinds experienced last year should begin to subside in the coming quarter. This cautious optimism enabled them to present a Q2 outlook that came a touch ahead of consensus (revenues of $2.3-2.4bln vs a consensus of $2.3bln; gross margin at 49.5-50.5% vs a consensus of 49.8%). In an otherwise directionless session, NXP was rewarded with a 14bps tightening of its 5 year risk premium at 179bps.»
[2] «The semi-conductor industry has been all over the news since the beginning of the week. After STM ( STMicorelectronics NV ) bidding for Fairchild on Tuesday, NXP ( NXP Semiconductors NV ) grabbed the headlines today, as they reported Q3 earnings. Although they announced revenue just below and EPS above the street consensus, people focused – as one would actually expect them to do - on the guidance for the coming quarters. The company surprised the market by lowering their forecast for fourth quarter revenues (which they expect to be down “low to upper teens range” quarter on quarter), as customers pulled back on orders amid a slowing global economy and higher inventories of unsold chips. The surprise did not come from the direction of the revision, but rather by its magnitude. The stock was first to react and was marked down almost 20%. The 5 year CDS was slower and only moved 12bps wider to 165bps. There is still time to reload shorts on a name that used to belong in the 200/250bps range.»
[3] «Most of the names traded sideways in the credit market today. Given the tight level of the risk premia, that can only be described as undeniable strength. One rare wobble came from NXP (NXP Semiconductors NV). The company announced overnight that it is buying Freescale for $11.8Bln, in a combination of cash and stocks. It aims to expand its market share in chips for cars, surfing the trend pointing to vehicle that drive themselves. The deal will be funded with $1bln and $1bln in new debt, which means an increase in net leverage from 1.7x to 3.5x. The initial reaction was to send the 5 year CDS 20bps wider. But there has been a lot of short risk positions built recently in the name in anticipation of such an operation, making it one of only a handful of names trading hardly tighter since the beginning of the year. Now that the deal turning two challengers into a giant has materialised, this CDS felt pretty squeezy and it closed only marginally wider at 149bps (it is wider as what some see on their screen as all the risk premia in DataGrapple are computed using a 100bps running coupon, and not the 500bps which is standard on NXP).»
[4] «Even though the momentum appears to slow a little bit in the CDS market, all indices had another positive session (with the exception of iTraxx Financial Senior which was 0.25bps wider…). The same was true for the vast majority of names, which saw their risk premia decrease across the board, and selling protection was a tough order today. One exception was NXPBV ( NXP Smeiconductors NV ). Its 5 year CDS was wider (up 2bps at 148bps), and the bid was relatively deep on the back on their $1bln convertible bond issue. The proceeds will be used to purchase stocks and repay existing loans. But if you take a few steps back and look at the 5 year CDS run over the last few years, you will see that the timing of the company is not that bad. The risk premium of NXP has never been that tight. The stock is also trading at its all-time high on the NASDAQ, and interest rates have never been that low. All in all, it is fair to say that the issue of convertible notes probably make sense… for them.»
[5] «Earlier in the year, the control of XRX ( Xerox Corp ) was handed to activist investors Icahn and Deason, after its planned merger with Fuji was scrapped. The company delivered today its first set of results under their guidance, and they were consistent with granting top priority to shareholder returns. The US photocopier which is facing a long running decline in its core business reported net profit that roughly halved and said revenue fell 5.8% year on year to $2.35Bln in the third quarter, below an average analyst estimate of $2.42Bln. Despite that fall, the new management increased its share-repurchase expectations by $200m, even though according to the different rating agencies, XRX does not have any further flexibility within its current BBB rating category. Investors certainly copied that, and sent XRX’s 5-year risk premium 27bps at 224bps.»

Question: What was NXP's revenue and gross margin outlook for Q2, and how did it compare to the consensus estimates?

Reasoning: Let's think step by step in order to produce the answer. We know from the context that NXP reported Q1 2016 numbers in line with analysts' expectations and presented a Q2 outlook that came a touch ahead of consensus. The revenues for Q2 were expected to be $2.3-2.4 billion, slightly above the consensus of $2.3 billion. The gross margin for Q2 was expected to be 49.5-50.5%, also slightly above the consensus of 49.8%.

Answer: The revenue outlook for Q2 was $2.3-2.4 billion, slightly above the consensus of $2.3 billion, and the gross margin outlook was 49.5-50.5%, slightly above the consensus of 49.8%.

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Context:
[1] «The bruising first quarter of 2016 is now a distant memory for investors who waged some of their money on commodity related companies.  AALLN (Anglo American Plc), MTNA ( ArcelorMittal SA ) and TKAGR ( ThyssenKrupp AG ) all reported in the last few weeks and beat analysts’ estimates. GLEINT ( Glencore Plc ) was the last to publish numbers yesterday, and they showed that they are also firing on all cylinders. The company swung back into the black in 2016, turning a pre-tax profit of $1.38bln. It reaped the benefits of rising prices in key commodities such as coal, zinc and copper, as well as falling costs and currency fluctuations. It beat consensus on its key target metrics, including net debt reduction. The latter had soared as high as $30bln in 2014 in the wake of the takeover of Xstrata. At the end of last year, it stood at $15.5bln - a reduction of $10.4bln in 12 months -, well below its own target. So much so that Mr Glasenberg said GLEINT should be able to “kick out a big dividend”. Credit investors have already been rewarded with a 1,200bps reduction in the company’s 5-year risk premium. From a high of 1,339bps in January 2016, it closed at 155bps today!»
[2] «ArcelorMittal (MTNA) was under pressure today, with its 5y CDS wider by c15bp and its stock down -5%. That happened despite ever higher metal prices YTD. The move was caused by the evacuation of c700 people living near its dormant Serra Azul tailing dam, as precautionary measure. The dam has not broken yet, but analysts are divided on how big the financial impact can be. On the other side of the Atlantic, the outlook is not much brighter. MTNA’s South African subsidiary AMSA has registered its 1st profit in ~10y thanks to higher margins (metal prices up), higher volumes, lower costs. But demand from South Africa is still sluggish (less growth in infrastructure projects) and imports from China have established a fierce competition. Despite bad news, it is not a credit story yet. Leverage is still around 1x and MTNA has a solid liquidity. It has $2.4B cash on its balance sheet and another $5.5B disponible from credit facilities. Net debt is higher $10.2B, but the short-term maturities ‘only’ amount to $3.2B ($1.3B CP $1.0B loans $0.9B bonds). Management’s commitment on leverage and ‘small’ dividend & buyback programme are also reassuring in the medium term. »
[3] «As you can see with the pale shades of the above grapple, it was a fairly dull day with most of Continental Europe out. A few names are flashing red though, and most of them are in the top right corner where the Energy and Basic Materials sectors can be found. Indeed, WFT ( Weatherford International Plc ) reported a larger than expected first quarter loss and lowered their free cash flow guidance for the year. That comes at a time where most people holding short risk positions have capitulated and sold the protection they owned during the aggressive rally we have seen since the February wides. As a results, there are very few investors left willing to offer CDS, and whenever buyers show up, risk premia are marked wider very rapidly. This technical factors go a long way towards explaining why WFT’s 5 year CDS was marked 7pts wider today. They also explains investors’ unease with the tight risk premia across the energy sector at the moment. If you extend the time span to a week (click on the "1W" toggle above the grapple) in the above grapple, you will see a number of names flashing red in that top right corner. WFT was not the first company to give up some of its recent impressive gains.»
[4] «As far as credit is concerned, despite some volatility, last week has not brought any major change. Utilities for instance were stable as a whole in Europe (you can see that the green candle is actually flat), but, within the sector, performance has varied greatly. Indeed, political issues have come back to the forefront in various countries, with a strong impact on a few names. In the UK, all eyes were on the outcome of the general election. With the election of a Conservative government, UK utilities reacted positively: the proposed power price freeze by the Labour Party leader had unsettled investors during the run-up to the poll. CENTRI’s ( Centrica Plc ) and UU’s ( United Utilities Group Plc ) 5 year risk premia tightened 6.5bps to 66bps and 1.5bps to 86.5bps respectively. At the other end of the spectrum, the never ending Greece saga continues and growing fears over the future of the country’s debt clearly did not help peripheral players. ELEPOR ( EDP - Energias De Portugal SA ) was the worst performer of the sector, and its 5 year CDS widened by 4bps to 133.5bps.»
[5] «Teck Resources (TCK) is a Canadian diversified mining company (copper, steelmaking coal, zinc and energy). Now in the tight quarter of the CDX HY, the name will be coming back in the CDX IG index from next March 20, after spending 3.5 years in HY. This is as TCK has been upgraded by Fitch to BBB- (=) in FEB and by Moody’s to Baa3 (=) in JAN. Now the 5y CDS pays c127bp, after ranging [100,200] over the past year or so. But due to its exposure to commodity price volatility, this CDS can be less well-behaved. In its history, this quite cyclical name has peaked ~1,500bp (GFC) and ~2,000bp (commo crash in 2015). It was a long way since then. Indeed, last year, TCK generated $4.9B EBITDA, from $4.3B $2.6B $1.6B the years before. FCF was +$1.1B from -$0.6B in 2015. Today it looks more credit-friendly than before the 2015 commodity slump, as dividend payout came from 50%+ to c5% now. Moreover, its net leverage has stabilized below 1x, over the past few quarters, and its liquidity position has improved to almost $6B. That being said, lower commodities, production disruptions, fast FCF burn due to high capex requirements remain potential risks. »

Question: How did FirstEnergy's cash reserves stand at the end of 2019, and what was the trend over the previous five years?

Reasoning: Let's think step by step in order to[32m produce the answer. We need to look at the information provided in the context regarding FirstEnergy's cash reserves at the end of 2019 and the trend over the previous five years.

Answer: The context does not provide information about FirstEnergy's cash reserves at the end of 2019 or the trend over the previous five years.[0m

third_compiled_rag("Did the OPEC members decide to cut oil production? When? Where? Why?").answer

'Yes, OPEC members decided to cut oil production during a meeting in Vienna. They agreed to reduce output by 1.2 million barrels per day and cap it at 32.5 million barrels per day in order to address the oversupply of oil in the market and support oil prices.'

dspy.settings.lm.inspect_history(n=1)

Based on the provided business and financial contexts, provide detailed and specific answers that fully address the posed questions, drawing explicitly from the information given.

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Follow the following format.

Context: may contain relevant facts

Question: ${question}

Reasoning: Let's think step by step in order to ${produce the answer}. We ...

Answer: ${answer}

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Context:
[1] «In February 2015, Ball agreed to acquire Rexam in a deal that would combine two of the world’s largest makers of cans for drinks, and it has been trying since to win regulatory approval for the deal. Today, it announced that it had agreed with Rexam to sell plants in Brazil and Europe together with innovation and support functions in Brazil, Britain, Germany, Switzerland and the United States to ARGID (Ardagh Packaging) to satisfy some of these regulatory concerns. This divestment program itself is subject to regulatory approvals, but it should enable ARGID to acquire assets with combined revenues of around $3bln and will cost the company approximately $3.4bln. To help finance the operation, ARGID will issue $2.85bln of secured and unsecured notes in the coming weeks. They had been rumoured to be the leading bidders for Ball-Rexam assets for a while, and that is the reason why the risk premium of ARGID never really participated in the rally that took iTraxx Crossover from 336bps on the April 8th to 294bps last Friday. Nevertheless, the quasi-certain perspective of more leverage led investors to mark ARGID’s 5 year CDS 36bps wider (to 452bps) today.»
[2] «This morning, BATSLN ( British American Tobacco Plc ) offered to merge with RAI ( Reynolds American, Inc. ). BATSLN already owns 42% of RAI, and the offer targets the remaining 58%. Over the past few years, several failed attempts were made to take full ownership with a view to create a geographically diverse group, since RAI is focused on the US and BATSLN is weighted towards everywhere else. There would also be scope for scale economies in R&D; for smokeless technologies, justifying the high multiples of the transaction – 16.3 times 12-month trailing Ebitda -. The current proposal values RAI at $56.5/share – that is a 20% premium to yesterday’s close – of which $24.13 will be cash, but discussions have not begun between the two companies and RAI was said to want a higher premium. In any case, the total cash contribution should be in excess of $20Bln and investors are expecting a heavy bond issuance to finance the deal which will increase leverage by roughly one time. They send BATSLN’s 5-year risk premium 7bps wider at 65bps without waiting for the final details of the operation.

Meanwhile, the broader credit market was quiet, and traded volumes on credit indices stood 30 to 40% below average. While risk premia were a touch wider across the board, moves were kept minimal.
»
[3] «According to various press reports, Altria, which controls 27% of SABLN ( SAB Miller Plc ), recently pulled out of a conference appearance to follow up on a similar action by ABIBB ( Anheuser Busch Inbev SA ). People were quick to think something was brewing and today ABIBB confirmed it had made an approach to SABLN’s board. This acquisition would be the biggest in the industry history and would cap more than a decade of consolidation across brewing companies, as consolidation has been used as a way to stave off a slowdown in established markets such as Europe and the US. Even though a deal is currently much more affordable because the price action of SABLN’s has been weak over the last 6 months, the deal would likely cost ABIBB more than $100bln. If both the stocks of both company reacted positively, that is a big number and investors pushed ABIBB’s 5 year risk premium 11bps wider to 79bps, the most in almost 3 years.»
[4] «Today, RAI (Reynolds American, Inc) and Lorillard Inc. announced that they are proceeding with their merger, in a $27.4bln deal that will see RAI become a bigger competitor to U.S. market leader MO (Altria Group Inc). In line with recent press speculation, IMTLN (Imperial Tobacco Group Plc) will acquire $7.1bln worth of assets from the merged entity. In the process, IMTLN will lever up to a level that is not compatible with an IG rating, and the market has recently punished them for that, putting today the final touch to a 20bps widening in a little less than 10 days. But the company has clearly discussed the transaction with the rating agencies before the announcement, and Moody’s was able to reaffirm their rating very quickly, whilst putting them on negative outlook. That last bit is there to make sure that they use some of their ridiculous cash generation towards deleveraging and that they respect a strict timescale to do so. With that in mind, most of the damage to the 5 year risk premium of IMTLN might already be behind us.»
[5] «Yesterday ABIBB ( Anheuser Busch Inbev SA ) made a formal $107bln offer to buy SABLN ( SABMiller plc ), sealing a long anticipated deal that combine the world’s biggest brewers to form a company that will have the number one or two position in 24 of the world’s 30 biggest beer markets according to analysts. To back the acquisition, ABIBB said it obtained the biggest corporate loan on record, $75bln. In the future part of the loans will be replaced by bonds, and the brewer is said to be planning to sell $55bln of bonds across multiple currency and maturities. When it happens, this sale will also be a record. It will exceed the $49bln of bonds that VZ ( Verizon Communications Inc ) issued two years ago to fund its buyout of VOD’s ( Vodafone Group plc ) stake in a wireless venture. But while in September 2013, VZ’s 5 year risk premium jumped 30bps from 70 to 100bps when the jumbo deal was announced, ABIBB’s 5 year risk premium hardly moved at all. It widened today by 2bps to 81bps, which is merely in line with the rest of the market ( iTraxx Main, the barometer of European investment grade credit, widened 3bps to 73.5bps).»

Question: What steps has Ball taken to win regulatory approval for its acquisition of Rexam?

Reasoning: Let's think step by step in order to produce the answer. We need to look at the context provided to understand the actions Ball has taken to win regulatory approval for its acquisition of Rexam.

Answer: Ball has agreed with Rexam to sell plants in Brazil and Europe, along with innovation and support functions in several countries, to ARGID (Ardagh Packaging) to satisfy regulatory concerns. This divestment program is subject to regulatory approvals and will enable ARGID to acquire assets with combined revenues of around $3 billion. Additionally, ARGID will issue $2.85 billion of secured and unsecured notes to help finance the operation.

---

Context:
[1] «Yesterday saw the earnings reporting season ‘s starting whistle for Q1 blown by AA ( Alcoa Inc ). They got things started after the closing bell, and the numbers were fairly mixed. While earnings came in above consensus, they missed on revenues. They also lowered their forecast for global demand of aluminium in 2016 – they expect it to climb 5% whereas previously their projection was a 6% increase – and they decreased their market deficit projection – they estimate a 1.1 million metric tons deficit in 2016 versus 1.2 million 3 months ago – as Chinese demand slows. That made AA one of the few names that saw their 5 year risk premium widen (+10bps at 405bps) in an otherwise constructive session. Even if it currently stands far off its February highs of 44%, it means the market assigns a 31% probability to an AA default over during the next 5 years.»
[2] «Like every other quarter, AA ( Alcoa Inc ) will be among the first to report at the very beginning of the second week of October. But while the results might get any scrutiny and generate some volatility as commodity related names have been under the spotlight recently, AA already proved over the last few days that it is not a credit for the faint hearted. On Monday, the company announced its plan to break itself in two by separating its manufacturing operations (the “Value-Add” business still to be named) from the legacy smelting and refining business (which will retain the Alcoa name) that is struggling to overcome booming production from China. Investors were unsure where the debt might end up, and they feared that the bonds might go to the less attractive part of the business, i.e. Alcoa. Accordingly, they sent AA’s 5 year risk premium 140bps wider at 457bps, meaning that a 5 year bond lost roughly 7cts on the dollar on that day. The company management issued a statement late yesterday saying that “Alcoa’s current intent is that the debt of Alcoa would be retained by the Value-Add company for which Alcoa is targeting an investment grade rating”. Guess what? AA’s 5 year risk premium closed at 335bps tonight.»
[3] «We already had a trickle of results in the past few weeks, but the reporting season begun in earnest today. Over the next few weeks, all major companies will be releasing third numbers on both sides of the Atlantic. As is now customary, AA ( Alcoa Inc ) kicked off the proceedings, and they did it in style. The 128-year-old company reported their last earnings before splitting into two entities - the newly branded Arconic which will service aerospace and automotive industries, and the new Alcoa which will focus on their mining and smelting operations in bauxite, alumina and aluminium products – and missed both earnings (32cts/share vs 34cts expected) and revenue (total sales fell to $5.2Bln vs $5.33Bln expected) estimates. More importantly, they lowered Q4 and year-end guidance, adding that next year’s outlook was “very hard” to read. Investors punished the stock and marked it 10% lower, while they sent AA’s 5-year risk premium 17.5bps wider at 250bps, still a long way away from the 650bps it reached at the very beginning of the year though.

Meanwhile, the broader credit market was on the back foot. Credit indices suffered as US equities felt the brunt of a strong dollar and the disappointing start to the earning season. In Europe iTraxx Main and iTraxx Crossover closed 1bp and 4.5bps  wider at 74.5bps and 335bps respectively, while, in the US, CDXIG was 1bp wider at 75bps and CDXHY 2bps wider at 404bps.
»
[4] «The reporting season is only beginning, and this used to be the time of the year when Alcoa was systematically mentioned as it used to be the first corporate to announce its results. Part of the company, which produces and distributes aerospace products, split from Alcoa Corporation, which produces and sells bauxite, alumina and aluminium products, and is now known as ARNC ( Arconic Inc ). It will report in a couple of weeks. Nevertheless, it attracted most of the headlines in the US credit market today. As of Friday, its stock was down 44% from the high it reached mid-January. It had suffered from tensions concerning the US aluminium and steel tariffs that have roiled metal producers. It is in this context that the Wall Street Journal reported late on Friday that private equity firms would be circling the company. Even though it is facing operational challenges, its weak cash generation and attractive valuation offer potential buyers a favourable entry point according to analysts. LBOs are synonymous of increased leverage, and while they sent ARNC’s stock 10% higher, investors pushed the company’s 5-year risk premium 85bps wider at 283bps.»
[5] «In August, ARNC ( Arconic Inc ) announced it was considering selling its building and construction systems unit which makes facades, windows and framing products. On Tuesday, when it reported better than expected quarterly profit and raised its full-year earnings forecast, it said the sale process was under way and had drawn robust interest. Under such circumstances, one would probably expect ARNC’s risk premium to tighten. But during the same earning meeting, the management also announced that the scope of the strategy and portfolio review initiated in February was being extended and its scope broadened “to address additional scenarios”. That gave weight to rumours that private equity firms are circling the company, with Apollo Global Management supposedly about to make an offer in excess of $11bln for the aluminium products maker. With LBO risks gathering and leverage potentially about to be propelled higher, investors in fact pushed ARNC’s 5-year risk premium above 400bps for the first time since 2016. »

Question: What adjustments did Alcoa Inc make to its global aluminum demand forecast for 2016?

Reasoning: Let's think step by step in order to produce the answer. We know from the context that Alcoa Inc adjusted its global aluminum demand forecast for 2016. They initially projected a 6% increase in demand but later revised it to a 5% increase. Additionally, they decreased their market deficit projection from 1.2 million metric tons to 1.1 million metric tons due to slowing Chinese demand.

Answer: Alcoa Inc adjusted its global aluminum demand forecast for 2016 by lowering it from a 6% increase to a 5% increase and decreasing their market deficit projection from 1.2 million metric tons to 1.1 million metric tons.

---

Context:
[1] «NXP ( NXP Semiconductors NV ) is a leading chip supplier for smartphones, but it is also a major supplier of chips for the auto industry following its recent acquisition of FSL (Freescale Semiconductor). These chips have a wide range of applications, from advanced driver assistance systems to “infotainment” or in-vehicle networking between different car systems. NXP’s strength in the auto industry is one of the reasons the company managed to surprise investors when it reported Q1 2016 numbers this morning, which were in line with analysts’ expectations. More importantly, even though management said the overall demand continues to be subdued, they also said that headwinds experienced last year should begin to subside in the coming quarter. This cautious optimism enabled them to present a Q2 outlook that came a touch ahead of consensus (revenues of $2.3-2.4bln vs a consensus of $2.3bln; gross margin at 49.5-50.5% vs a consensus of 49.8%). In an otherwise directionless session, NXP was rewarded with a 14bps tightening of its 5 year risk premium at 179bps.»
[2] «The semi-conductor industry has been all over the news since the beginning of the week. After STM ( STMicorelectronics NV ) bidding for Fairchild on Tuesday, NXP ( NXP Semiconductors NV ) grabbed the headlines today, as they reported Q3 earnings. Although they announced revenue just below and EPS above the street consensus, people focused – as one would actually expect them to do - on the guidance for the coming quarters. The company surprised the market by lowering their forecast for fourth quarter revenues (which they expect to be down “low to upper teens range” quarter on quarter), as customers pulled back on orders amid a slowing global economy and higher inventories of unsold chips. The surprise did not come from the direction of the revision, but rather by its magnitude. The stock was first to react and was marked down almost 20%. The 5 year CDS was slower and only moved 12bps wider to 165bps. There is still time to reload shorts on a name that used to belong in the 200/250bps range.»
[3] «Most of the names traded sideways in the credit market today. Given the tight level of the risk premia, that can only be described as undeniable strength. One rare wobble came from NXP (NXP Semiconductors NV). The company announced overnight that it is buying Freescale for $11.8Bln, in a combination of cash and stocks. It aims to expand its market share in chips for cars, surfing the trend pointing to vehicle that drive themselves. The deal will be funded with $1bln and $1bln in new debt, which means an increase in net leverage from 1.7x to 3.5x. The initial reaction was to send the 5 year CDS 20bps wider. But there has been a lot of short risk positions built recently in the name in anticipation of such an operation, making it one of only a handful of names trading hardly tighter since the beginning of the year. Now that the deal turning two challengers into a giant has materialised, this CDS felt pretty squeezy and it closed only marginally wider at 149bps (it is wider as what some see on their screen as all the risk premia in DataGrapple are computed using a 100bps running coupon, and not the 500bps which is standard on NXP).»
[4] «Even though the momentum appears to slow a little bit in the CDS market, all indices had another positive session (with the exception of iTraxx Financial Senior which was 0.25bps wider…). The same was true for the vast majority of names, which saw their risk premia decrease across the board, and selling protection was a tough order today. One exception was NXPBV ( NXP Smeiconductors NV ). Its 5 year CDS was wider (up 2bps at 148bps), and the bid was relatively deep on the back on their $1bln convertible bond issue. The proceeds will be used to purchase stocks and repay existing loans. But if you take a few steps back and look at the 5 year CDS run over the last few years, you will see that the timing of the company is not that bad. The risk premium of NXP has never been that tight. The stock is also trading at its all-time high on the NASDAQ, and interest rates have never been that low. All in all, it is fair to say that the issue of convertible notes probably make sense… for them.»
[5] «Earlier in the year, the control of XRX ( Xerox Corp ) was handed to activist investors Icahn and Deason, after its planned merger with Fuji was scrapped. The company delivered today its first set of results under their guidance, and they were consistent with granting top priority to shareholder returns. The US photocopier which is facing a long running decline in its core business reported net profit that roughly halved and said revenue fell 5.8% year on year to $2.35Bln in the third quarter, below an average analyst estimate of $2.42Bln. Despite that fall, the new management increased its share-repurchase expectations by $200m, even though according to the different rating agencies, XRX does not have any further flexibility within its current BBB rating category. Investors certainly copied that, and sent XRX’s 5-year risk premium 27bps at 224bps.»

Question: What did NXP's management say about the demand and market conditions during their earnings report?

Reasoning: Let's think step by step in order to produce the answer. We need to look at the information provided in the context regarding NXP's earnings report.

Answer: The management of NXP stated that overall demand continues to be subdued, but they also mentioned that the headwinds experienced in the previous year should begin to subside in the coming quarter.

---

Context:
[1] «NXP ( NXP Semiconductors NV ) is a leading chip supplier for smartphones, but it is also a major supplier of chips for the auto industry following its recent acquisition of FSL (Freescale Semiconductor). These chips have a wide range of applications, from advanced driver assistance systems to “infotainment” or in-vehicle networking between different car systems. NXP’s strength in the auto industry is one of the reasons the company managed to surprise investors when it reported Q1 2016 numbers this morning, which were in line with analysts’ expectations. More importantly, even though management said the overall demand continues to be subdued, they also said that headwinds experienced last year should begin to subside in the coming quarter. This cautious optimism enabled them to present a Q2 outlook that came a touch ahead of consensus (revenues of $2.3-2.4bln vs a consensus of $2.3bln; gross margin at 49.5-50.5% vs a consensus of 49.8%). In an otherwise directionless session, NXP was rewarded with a 14bps tightening of its 5 year risk premium at 179bps.»
[2] «The semi-conductor industry has been all over the news since the beginning of the week. After STM ( STMicorelectronics NV ) bidding for Fairchild on Tuesday, NXP ( NXP Semiconductors NV ) grabbed the headlines today, as they reported Q3 earnings. Although they announced revenue just below and EPS above the street consensus, people focused – as one would actually expect them to do - on the guidance for the coming quarters. The company surprised the market by lowering their forecast for fourth quarter revenues (which they expect to be down “low to upper teens range” quarter on quarter), as customers pulled back on orders amid a slowing global economy and higher inventories of unsold chips. The surprise did not come from the direction of the revision, but rather by its magnitude. The stock was first to react and was marked down almost 20%. The 5 year CDS was slower and only moved 12bps wider to 165bps. There is still time to reload shorts on a name that used to belong in the 200/250bps range.»
[3] «Most of the names traded sideways in the credit market today. Given the tight level of the risk premia, that can only be described as undeniable strength. One rare wobble came from NXP (NXP Semiconductors NV). The company announced overnight that it is buying Freescale for $11.8Bln, in a combination of cash and stocks. It aims to expand its market share in chips for cars, surfing the trend pointing to vehicle that drive themselves. The deal will be funded with $1bln and $1bln in new debt, which means an increase in net leverage from 1.7x to 3.5x. The initial reaction was to send the 5 year CDS 20bps wider. But there has been a lot of short risk positions built recently in the name in anticipation of such an operation, making it one of only a handful of names trading hardly tighter since the beginning of the year. Now that the deal turning two challengers into a giant has materialised, this CDS felt pretty squeezy and it closed only marginally wider at 149bps (it is wider as what some see on their screen as all the risk premia in DataGrapple are computed using a 100bps running coupon, and not the 500bps which is standard on NXP).»
[4] «Even though the momentum appears to slow a little bit in the CDS market, all indices had another positive session (with the exception of iTraxx Financial Senior which was 0.25bps wider…). The same was true for the vast majority of names, which saw their risk premia decrease across the board, and selling protection was a tough order today. One exception was NXPBV ( NXP Smeiconductors NV ). Its 5 year CDS was wider (up 2bps at 148bps), and the bid was relatively deep on the back on their $1bln convertible bond issue. The proceeds will be used to purchase stocks and repay existing loans. But if you take a few steps back and look at the 5 year CDS run over the last few years, you will see that the timing of the company is not that bad. The risk premium of NXP has never been that tight. The stock is also trading at its all-time high on the NASDAQ, and interest rates have never been that low. All in all, it is fair to say that the issue of convertible notes probably make sense… for them.»
[5] «Earlier in the year, the control of XRX ( Xerox Corp ) was handed to activist investors Icahn and Deason, after its planned merger with Fuji was scrapped. The company delivered today its first set of results under their guidance, and they were consistent with granting top priority to shareholder returns. The US photocopier which is facing a long running decline in its core business reported net profit that roughly halved and said revenue fell 5.8% year on year to $2.35Bln in the third quarter, below an average analyst estimate of $2.42Bln. Despite that fall, the new management increased its share-repurchase expectations by $200m, even though according to the different rating agencies, XRX does not have any further flexibility within its current BBB rating category. Investors certainly copied that, and sent XRX’s 5-year risk premium 27bps at 224bps.»

Question: What was NXP's revenue and gross margin outlook for Q2, and how did it compare to the consensus estimates?

Reasoning: Let's think step by step in order to produce the answer. We know from the context that NXP reported Q1 2016 numbers in line with analysts' expectations and presented a Q2 outlook that came a touch ahead of consensus. The revenues for Q2 were expected to be $2.3-2.4 billion, slightly above the consensus of $2.3 billion. The gross margin for Q2 was expected to be 49.5-50.5%, also slightly above the consensus of 49.8%.

Answer: The revenue outlook for Q2 was $2.3-2.4 billion, slightly above the consensus of $2.3 billion, and the gross margin outlook was 49.5-50.5%, slightly above the consensus of 49.8%.

---

Context:
[1] «OPEC members were holding a meeting in Vienna today. They managed to confound skeptics and reached an agreement to cut oil supply, their first such move in eight years. Members of the organisation will reduce output by 1.2mln barrels per day and cap it to 32.5mln per day. With the prospect of less pumping – the agreement should come into force at the beginning of 2017 -, oil prices soared by 8%, and Brent was back above $50/barrel. After weeks of tense negotiations, the agreement, which also calls for a reduction of about 600,000 barrels a day by non-OPEC countries, was taken at face value by investors as Russia, the biggest producer outside the organisation, said it is ready to participate marking a reversal of its previous position. January is still far away and December will see ongoing record production, but that was brushed aside and the whole energy sector benefitted from the news, outperforming the rest of the market. »
[2] «Part of continental Europe was closed today and it had a bearing on the market activity, and volumes were on the low side. People spent the whole day keeping an eye on headlines regarding the meeting of OPEC and their allies in Vienna. Hardly anything filtered until the middle of the afternoon when the producer group together with Russia and other non-members announced they had agreed to prolong their previous agreement to limit output for another 9 months. While 6 months ago, the cartel surprised investors and delivered cuts that exceeded expectations, the market did not seem entirely convinced today. People were apparently left a bit disappointed and news that Equatorial Guinea was officially becoming the 14th member of OPEC – it will be one of the smallest producers, pumping 270,000 barrels a day – was not the extra something people had been hoping for. Without a steer on what will happen beyond March, there is concern that OPEC could return to the free-for-all production that caused the free fall of oil between mid-2014 and early 2016. While oil had been stable for most of the day, it lost roughly 4% during the last couple of hours of trading. That was a drag on all commodity names, which today formed the worst performing group in the US high yield universe.»
[3] «Over the weekend, all eyes were on the long awaited oil producers meeting in Doha. It ended in disappointment, as OPEC members and major producers walked away without any agreement on a production freeze. That initially sent oil 7% lower and markets were weaker across the board. However, as the day progressed, investors holding short risk positions used that blip as an opportunity to reduce their exposure ahead of the performance of Mr Draghi on Thursday. The market was also helped by a strike in Kuwait, which will temporarily limit their oil production by 2/3 and sent the barrel back to unchanged on the day. In the end, despite the worst possible outcome regarding oil output, names that saw their risk premium increase on the day were few and far between. Credit indices and their fair values closed tighter in investment grade and high yield alike, giving the above grapple its greenish colour.»
[4] «Crude oil prices collapsed today again, on signs of record Saudi output, high inventories across the globe, risks of China’s economy slowing down, Iran effectively able to export thanks to waivers, and weaker risk sentiment. WTI lost almost 8% on the day, and around 34% since the October 3rd peak. Brent followed south $60/bbl. for the first time in a year. As a consequence, US IG energy companies (exploration & production, refiners, marketers, drillers) are underperforming today, their 5y CDS widening by 7/10bp. While Saudi Arabia and other OPEC members reportedly plan quiet output cuts to avoid Trump's ire, Russia doesn’t seem in a rush to join this move. Listening to Trump’s recent speeches, it is clear that he considers the c35% lower gasoline prices in the US a key achievement. Therefore he may well continue to pressure Saudis to not cut output, ahead of the OPEC meeting scheduled on Dec 6th in Vienna. Coincidentally, Trump has recently declared that he would support the Saudis despite his own intelligence agency concluded that Saudi crown prince MBS ordered Khashoggi’s murder. »
[5] «In January, West Texas Intermediate (WTI) was trading at $56/barrel after OPEC curbs drove oil to its highest level in two years. But it looks as if the concerted efforts of the group to cut production is failing to clear a surplus of crude. Indeed, lured by rising prices, US shale drillers have been adding rigs every week recently and have pushed US oil production to its highest since August 2015. Libya – which is not an OPEC member - also announced that their production reached its highest since 2014. At the same time, US gasoline demand is pretty weak – in April it was down 2.7% from the same period a year earlier – and investors fear tighter credit conditions in China could depress growth in the coming months. Doubts over the ability of OPEC to durably influence prices are rising fast, and WTI has been on a downward trajectory over the last three. The pace accelerated yesterday with a 5% loss in a single session to $45.5. Even if the level of crude stabilised today, this was enough to spook investors, and they remarked the risk premium of the whole energy complex. This was true on both sides of the Atlantic, but US names were the most affected as a number of them are high beta credits.»

Question: Did the OPEC members decide to cut oil productions? When? Where? Why?

Reasoning: Let's think step by step in order to[32m produce the answer. From the context provided, we know that OPEC members did decide to cut oil production. The decision was made during a meeting in Vienna, where they agreed to reduce output by 1.2 million barrels per day and cap it at 32.5 million barrels per day. This decision was made to address the oversupply of oil in the market and support oil prices.

Answer: Yes, OPEC members decided to cut oil production during a meeting in Vienna. They agreed to reduce output by 1.2 million barrels per day and cap it at 32.5 million barrels per day in order to address the oversupply of oil in the market and support oil prices.[0m

evaluate(third_compiled_rag, metric=llm_metric)

  0%|          | 0/10 [00:00<?, ?it/s]

Test Question: What sectors does NXP Semiconductors NV primarily supply chips for?
Predicted Answer: NXP Semiconductors NV primarily supplies chips for the auto industry, specifically for applications in advanced driver assistance systems, infotainment, and in-vehicle networking between different car systems.


Average Metric: 4.0 / 1  (400.0):  10%|█         | 1/10 [00:00<00:02,  3.02it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What are some of the applications of NXP's chips in the auto industry?
Predicted Answer: NXP's chips in the auto industry have applications ranging from advanced driver assistance systems to "infotainment" and in-vehicle networking between different car systems.


Average Metric: 8.0 / 2  (400.0):  20%|██        | 2/10 [00:00<00:02,  3.00it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: How did NXP's Q1 2016 earnings report compare to analysts' expectations?
Predicted Answer: NXP's Q1 2016 earnings report was in line with analysts' expectations. The company surprised investors with numbers that met the consensus. Additionally, their Q2 outlook slightly exceeded consensus estimates, with revenues expected to be $2.3-2.4 billion compared to a consensus of $2.3 billion and a gross margin of 49.5-50.5% versus a consensus of 49.8%.


Average Metric: 12.0 / 3  (400.0):  30%|███       | 3/10 [00:01<00:02,  2.88it/s]

Faithful: 5
Detail: 5
Overall: 5


Average Metric: 16.0 / 4  (400.0):  30%|███       | 3/10 [00:01<00:02,  2.88it/s]

Test Question: What did NXP's management say about the demand and market conditions during their earnings report?
Predicted Answer: NXP's management mentioned that overall demand continues to be subdued, but they also stated that the headwinds experienced last year should begin to subside in the coming quarter. This cautious optimism led them to present a Q2 outlook that slightly exceeded consensus estimates for revenues and gross margin.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 20.0 / 5  (400.0):  50%|█████     | 5/10 [00:01<00:01,  2.70it/s]

Test Question: What was NXP's revenue and gross margin outlook for Q2, and how did it compare to the consensus estimates?
Predicted Answer: The revenue outlook for Q2 was $2.3-2.4 billion, slightly above the consensus of $2.3 billion, and the gross margin outlook was 49.5-50.5%, slightly above the consensus of 49.8%.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 24.0 / 6  (400.0):  60%|██████    | 6/10 [00:02<00:01,  2.74it/s]

Test Question: What steps has Ball taken to win regulatory approval for its acquisition of Rexam?
Predicted Answer: Ball has agreed with Rexam to sell plants in Brazil and Europe, along with innovation and support functions in several countries, to ARGID (Ardagh Packaging) to satisfy regulatory concerns. This divestment program is subject to regulatory approvals and will enable ARGID to acquire assets with combined revenues of around $3 billion. Additionally, ARGID will issue $2.85 billion of secured and unsecured notes to help finance the operation.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 28.0 / 7  (400.0):  70%|███████   | 7/10 [00:02<00:01,  2.72it/s]

Test Question: Who is acquiring the assets divested by Ball and Rexam, and what is the significance of this acquisition?
Predicted Answer: The assets divested by Ball and Rexam are being acquired by ARGID (Ardagh Packaging). This acquisition is significant as it allows ARGID to acquire assets with combined revenues of around $3 billion, costing the company approximately $3.4 billion, and will be financed through the issuance of $2.85 billion of secured and unsecured notes.
Faithful: 5
Detail: 5
Overall: 5
Test Question: Why did ARGID's risk premium not participate in the recent rally of the iTraxx Crossover index?
Predicted Answer: ARGID's risk premium did not participate in the recent rally of the iTraxx Crossover index because they were rumored to be the leading bidders for Ball-Rexam assets, which led investors to mark their 5-year CDS wider. Additionally, the perspective of more leverage due to the acquisition of assets from Ball-Rexam also contributed to investors widening ARGID's risk premium.


Average Metric: 29.6 / 8  (370.0):  80%|████████  | 8/10 [00:02<00:00,  2.60it/s]

Faithful: 1
Detail: 5
Overall: 1
Test Question: How did the market react to the announcement regarding ARGID's involvement in the Ball-Rexam deal?
Predicted Answer: The market reacted to the announcement of ARGID's involvement in the Ball-Rexam deal by widening ARGID's 5-year CDS risk premium by 36bps to 452bps.


Average Metric: 33.6 / 9  (373.3):  90%|█████████ | 9/10 [00:03<00:00,  2.64it/s]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What adjustments did Alcoa Inc make to its global aluminum demand forecast for 2016?
Predicted Answer: Alcoa Inc adjusted its global aluminum demand forecast for 2016 by lowering it from a projected 6% increase to a 5% increase. Additionally, they decreased their market deficit projection from 1.2 million metric tons to 1.1 million metric tons due to slowing Chinese demand.


Average Metric: 37.6 / 10  (376.0): 100%|██████████| 10/10 [00:03<00:00,  2.68it/s]

Faithful: 5
Detail: 5
Overall: 5
Average Metric: 37.6 / 10  (376.0%)

	question	answer	llm_metric
0	What sectors does NXP Semiconductors NV primarily supply chips for?	NXP Semiconductors NV primarily supplies chips for the auto industry, specifically for applications in advanced driver assistance systems, infotainment, and in-vehicle networking between different car...	4.0
1	What are some of the applications of NXP's chips in the auto industry?	NXP's chips in the auto industry have applications ranging from advanced driver assistance systems to "infotainment" and in-vehicle networking between different car systems.	4.0
2	How did NXP's Q1 2016 earnings report compare to analysts' expectations?	NXP's Q1 2016 earnings report was in line with analysts' expectations. The company surprised investors with numbers that met the consensus. Additionally, their Q2 outlook...	4.0
3	What did NXP's management say about the demand and market conditions during their earnings report?	NXP's management mentioned that overall demand continues to be subdued, but they also stated that the headwinds experienced last year should begin to subside in...	4.0
4	What was NXP's revenue and gross margin outlook for Q2, and how did it compare to the consensus estimates?	The revenue outlook for Q2 was $2.3-2.4 billion, slightly above the consensus of $2.3 billion, and the gross margin outlook was 49.5-50.5%, slightly above the...	4.0

... 5 more rows not displayed ...

376.0

round(376 / 348 , 2), "larger metric improvement with this 'optimization'..."

(1.08, "larger metric improvement with this 'optimization'...")

Final evaluation on the `test` set

from dspy.evaluate.evaluate import Evaluate

evaluate = Evaluate(devset=testset, num_threads=1, display_progress=True, display_table=5)

Let’s observe how the un-optimized baseline RAG performs on the test set:

evaluate(uncompiled_rag, metric=llm_metric)

  0%|          | 0/20 [00:00<?, ?it/s]

Test Question: How did Alcoa Inc revise its market deficit projection for 2016?
Predicted Answer: Alcoa Inc revised its market deficit projection for 2016 to 1.1 million metric tons.


Average Metric: 3.6 / 1  (360.0):   5%|▌         | 1/20 [00:00<00:09,  1.99it/s]

Faithful: 5
Detail: 3
Overall: 5
Test Question: What significant financial move did ArcelorMittal SA announce?
Predicted Answer: ArcelorMittal SA announced a $3 billion capital increase alongside the sale of a $1 billion stake in Spanish auto-parts maker Gestamp.


Average Metric: 7.4 / 2  (370.0):  10%|█         | 2/20 [00:00<00:08,  2.12it/s]

Faithful: 5
Detail: 4
Overall: 5
Test Question: At what levels did ArcelorMittal SA propose to buy back their bonds, and how was this received?
Predicted Answer: ArcelorMittal SA proposed to buy back their bonds at levels substantially above the levels at which they were trading in the secondary market. This move was received positively by investors.


Average Metric: 11.2 / 3  (373.3):  15%|█▌        | 3/20 [00:01<00:07,  2.28it/s]

Faithful: 5
Detail: 4
Overall: 5
Test Question: How did investors react to ArcelorMittal's bond buyback announcement?
Predicted Answer: Investors reacted positively to ArcelorMittal's bond buyback announcement by sending the 5-year CDS to its tightest level since the March roll.


Average Metric: 15.0 / 4  (375.0):  20%|██        | 4/20 [00:01<00:08,  1.94it/s]

Faithful: 5
Detail: 4
Overall: 5


Average Metric: 18.6 / 5  (372.0):  25%|██▌       | 5/20 [00:02<00:07,  2.11it/s]

Test Question: Who owns and manages OTE (Hellenic Telecommunications Organisation SA)?
Predicted Answer: OTE (Hellenic Telecommunications Organisation SA) is owned and managed by Germany's DT (Deutsche Telekom AG).
Faithful: 5
Detail: 3
Overall: 5


Average Metric: 22.6 / 6  (376.7):  30%|███       | 6/20 [00:02<00:06,  2.31it/s]

Test Question: What event influenced investor sentiment positively despite OTE's earnings?
Predicted Answer: The positive tone at the Eurogroup meeting, where debt relief options for Greece were discussed earlier than expected, influenced investor sentiment positively despite OTE's earnings.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 26.6 / 7  (380.0):  35%|███▌      | 7/20 [00:03<00:05,  2.48it/s]

Test Question: How did the positive developments at the Eurogroup meeting affect OTE’s financial market indicators?
Predicted Answer: The positive developments at the Eurogroup meeting caused OTE's 5 year risk premium to tighten by 35bps to 431bps.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 30.200000000000003 / 8  (377.5):  40%|████      | 8/20 [00:03<00:05,  2.24it/s]

Test Question: What allegations are connected to the investigation surrounding Ohio House Bill 6?
Predicted Answer: The context provided does not mention any allegations connected to the investigation surrounding Ohio House Bill 6.
Faithful: 5
Detail: 3
Overall: 5


Average Metric: 33.6 / 9  (373.3):  45%|████▌     | 9/20 [00:03<00:04,  2.37it/s]

Test Question: What were the consequences for FirstEnergy's leadership following the federal corruption scandal?
Predicted Answer: The consequences for FirstEnergy's leadership following a federal corruption scandal are not mentioned in the context.
Faithful: 5
Detail: 2
Overall: 5


Average Metric: 33.6 / 10  (336.0):  50%|█████     | 10/20 [00:04<00:04,  2.48it/s]

Test Question: What financial operations were highlighted for FirstEnergy in 2019?
Predicted Answer: The financial operations highlighted for FirstEnergy in 2019 were not mentioned in the provided context.
Faithful: No
Detail: 1
Overall: 5
Error for example in dev set:          could not convert string to float: 'No'
Test Question: How did the market react to Novafives' third-quarter performance and outlook?
Predicted Answer: The market reacted negatively to Novafives' third-quarter performance and outlook, with their bonds being punished and indicated approximately 6pts lower on the day.


Average Metric: 37.6 / 11  (341.8):  55%|█████▌    | 11/20 [00:16<00:35,  3.92s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What specific issues did Novafives report in their third-quarter results?
Predicted Answer: Novafives reported weak third-quarter results, with the Ebitda margin guidance for 2018 revised down to 5% and the covenant net leverage increasing to 5.8 times.


Average Metric: 41.6 / 12  (346.7):  60%|██████    | 12/20 [00:29<00:54,  6.76s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What financial action did Vue International (VUECIN) take as December approaches?
Predicted Answer: Vue International (VUECIN) decided to issue a 7-year facility consisting of two tranches, one in Euro and one in GBP, to finance the acquisition of Cinestar, new site capital expenditures, and to refinance existing debt.


Average Metric: 45.6 / 13  (350.8):  65%|██████▌   | 13/20 [00:41<00:58,  8.39s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What are the intended uses of the proceeds from Vue International's new term loan?
Predicted Answer: The intended uses of the proceeds from Vue International's new term loan are to finance the acquisition of Cinestar, new site capital expenditures, and to refinance existing debt.


Average Metric: 49.6 / 14  (354.3):  70%|███████   | 14/20 [00:53<00:57,  9.54s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What will happen to VUECIN’s existing debt instruments as a result of the new term loan?
Predicted Answer: VUECIN's existing debt instruments will be entirely taken out and replaced by the proceeds from the new term loan.


Average Metric: 53.2 / 15  (354.7):  75%|███████▌  | 15/20 [01:08<00:55, 11.03s/it]

Faithful: 5
Detail: 3
Overall: 5
Test Question: How will the new debt issuance affect the deliverables into VUECIN’s CDS contracts?
Predicted Answer: The new debt issuance by VUECIN will result in only loans being deliverable into CDS contracts, as the existing bonds will be entirely taken out.


Average Metric: 56.800000000000004 / 16  (355.0):  80%|████████  | 16/20 [01:20<00:45, 11.46s/it]

Faithful: 5
Detail: 3
Overall: 5
Test Question: What was the impact on VUECIN’s leverage due to the new financing?
Predicted Answer: The impact on VUECIN’s leverage due to the new financing was that it remained flat at 5.5x.


Average Metric: 60.400000000000006 / 17  (355.3):  85%|████████▌ | 17/20 [01:33<00:35, 11.73s/it]

Faithful: 5
Detail: 3
Overall: 5
Test Question: Why is BATSLN particularly vulnerable to the FDA’s potential regulation on menthol cigarettes?
Predicted Answer: BATSLN is particularly vulnerable to the FDA's potential regulation on menthol cigarettes because it produces Newport, the best-selling brand of menthol cigarettes in the US, and menthol cigarettes represent a significant portion of its profit.


Average Metric: 64.4 / 18  (357.8):  90%|█████████ | 18/20 [01:46<00:24, 12.09s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: How did BATSLN's stock respond to the news of potential FDA restrictions on menthol cigarettes?
Predicted Answer: The news of potential FDA restrictions on menthol cigarettes caused BATSLN's stock to fall by 11%.


Average Metric: 68.0 / 19  (357.9):  95%|█████████▌| 19/20 [01:59<00:12, 12.44s/it]

Faithful: 5
Detail: 3
Overall: 5
Test Question: What other factor has contributed to the increased caution from investors towards BATSLN?
Predicted Answer: The higher leverage of BATSLN since the 2017 merger with RAI has contributed to the increased caution from investors towards the company.


Average Metric: 72.0 / 20  (360.0): 100%|██████████| 20/20 [02:10<00:00,  6.51s/it]

Faithful: 5
Detail: 5
Overall: 5
Average Metric: 72.0 / 20  (360.0%)

	question	answer	llm_metric
0	How did Alcoa Inc revise its market deficit projection for 2016?	Alcoa Inc revised its market deficit projection for 2016 to 1.1 million metric tons.	3.6
1	What significant financial move did ArcelorMittal SA announce?	ArcelorMittal SA announced a $3 billion capital increase alongside the sale of a $1 billion stake in Spanish auto-parts maker Gestamp.	3.8
2	At what levels did ArcelorMittal SA propose to buy back their bonds, and how was this received?	ArcelorMittal SA proposed to buy back their bonds at levels substantially above the levels at which they were trading in the secondary market. This move...	3.8
3	How did investors react to ArcelorMittal's bond buyback announcement?	Investors reacted positively to ArcelorMittal's bond buyback announcement by sending the 5-year CDS to its tightest level since the March roll.	3.8
4	Who owns and manages OTE (Hellenic Telecommunications Organisation SA)?	OTE (Hellenic Telecommunications Organisation SA) is owned and managed by Germany's DT (Deutsche Telekom AG).	3.6

... 15 more rows not displayed ...

360.0

The first ‘optimized’ RAG using BootstrapFewShot is actually performing worse on the test set:

BootstrapFewShotWithRandomSearch was performing better on the test set. I omitted this model for the sake of brevity since similar to BootstrapFewShot in essence (+ random search).

evaluate(compiled_rag, metric=llm_metric)

  0%|          | 0/20 [00:00<?, ?it/s]

Test Question: How did Alcoa Inc revise its market deficit projection for 2016?
Predicted Answer: Alcoa Inc revised its market deficit projection for 2016 to a 1.1 million metric tons deficit.


Average Metric: 3.6 / 1  (360.0):   5%|▌         | 1/20 [00:00<00:09,  1.93it/s]

Faithful: 5
Detail: 3
Overall: 5


Average Metric: 7.4 / 2  (370.0):  10%|█         | 2/20 [00:00<00:07,  2.28it/s]

Test Question: What significant financial move did ArcelorMittal SA announce?
Predicted Answer: ArcelorMittal SA announced a $3 billion capital increase alongside the sale of a $1 billion stake in Spanish auto-parts maker Gestamp.
Faithful: 5
Detail: 4
Overall: 5


Average Metric: 11.4 / 3  (380.0):  15%|█▌        | 3/20 [00:01<00:07,  2.41it/s]

Test Question: At what levels did ArcelorMittal SA propose to buy back their bonds, and how was this received?
Predicted Answer: ArcelorMittal SA proposed to buy back their bonds at levels substantially above where they were trading in the secondary market, and this was positively received by investors.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 15.4 / 4  (385.0):  20%|██        | 4/20 [00:01<00:06,  2.51it/s]

Test Question: How did investors react to ArcelorMittal's bond buyback announcement?
Predicted Answer: Investors reacted positively to ArcelorMittal's bond buyback announcement, sending the company's 5-year CDS to its tightest level since the March roll.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 19.0 / 5  (380.0):  25%|██▌       | 5/20 [00:02<00:05,  2.64it/s]

Test Question: Who owns and manages OTE (Hellenic Telecommunications Organisation SA)?
Predicted Answer: OTE (Hellenic Telecommunications Organisation SA) is owned and managed by Germany's DT (Deutsche Telekom AG).
Faithful: 5
Detail: 3
Overall: 5
Test Question: What event influenced investor sentiment positively despite OTE's earnings?
Predicted Answer: The positive tone at the Eurogroup meeting, discussing debt relief options for Greece, influenced investor sentiment positively despite OTE's earnings.


Average Metric: 22.6 / 6  (376.7):  30%|███       | 6/20 [00:02<00:05,  2.36it/s]

Faithful: 5
Detail: 3
Overall: 5
Test Question: How did the positive developments at the Eurogroup meeting affect OTE’s financial market indicators?
Predicted Answer: The positive developments at the Eurogroup meeting led to OTE’s 5-year risk premium tightening by 35bps to 431bps.


Average Metric: 26.400000000000002 / 7  (377.1):  35%|███▌      | 7/20 [00:02<00:05,  2.47it/s]

Faithful: 5
Detail: 4
Overall: 5
Test Question: What allegations are connected to the investigation surrounding Ohio House Bill 6?
Predicted Answer: The allegations connected to the investigation surrounding Ohio House Bill 6 involve tampering with the slaughter date of poultry being processed at sites that supply retailers including Tesco, Sainsbury’s, Marks & Spencer, Aldi, and Lidl.


Average Metric: 27.6 / 8  (345.0):  40%|████      | 8/20 [00:03<00:04,  2.64it/s]

Faithful: 1
Detail: 3
Overall: 1
Test Question: What were the consequences for FirstEnergy's leadership following the federal corruption scandal?
Predicted Answer: The consequences for FirstEnergy's leadership following the federal corruption scandal were not explicitly mentioned in the provided context.


Average Metric: 27.6 / 9  (306.7):  45%|████▌     | 9/20 [00:03<00:04,  2.58it/s]

Faithful: No
Detail: 2
Overall: 5
Error for example in dev set:          could not convert string to float: 'No'


Average Metric: 29.200000000000003 / 10  (292.0):  50%|█████     | 10/20 [00:04<00:03,  2.54it/s]

Test Question: What financial operations were highlighted for FirstEnergy in 2019?
Predicted Answer: There is no information provided regarding any financial operations highlighted for FirstEnergy in 2019 in the context.
Faithful: 1
Detail: 1
Overall: 5
Test Question: How did the market react to Novafives' third-quarter performance and outlook?
Predicted Answer: The market reacted negatively to Novafives' third-quarter performance and outlook, with their bonds being indicated approximately 6 points lower on the day.


Average Metric: 33.2 / 11  (301.8):  55%|█████▌    | 11/20 [00:15<00:34,  3.89s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What specific issues did Novafives report in their third-quarter results?
Predicted Answer: Novafives reported weak third-quarter results, an increase in covenant net leverage, and revised Ebitda margin guidance for 2018.


Average Metric: 36.800000000000004 / 12  (306.7):  60%|██████    | 12/20 [00:30<00:58,  7.29s/it]

Faithful: 5
Detail: 3
Overall: 5
Test Question: What financial action did Vue International (VUECIN) take as December approaches?
Predicted Answer: Vue International (VUECIN) decided to issue a 7-year facility consisting of two tranches to finance the acquisition of Cinestar, new site capital expenditures, and to refinance existing debt as December approaches.


Average Metric: 40.800000000000004 / 13  (313.8):  65%|██████▌   | 13/20 [00:43<01:01,  8.85s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What are the intended uses of the proceeds from Vue International's new term loan?
Predicted Answer: The proceeds from Vue International's new term loan will be used to finance the acquisition of Cinestar, new site capital expenditures, and to refinance existing debt.


Average Metric: 44.800000000000004 / 14  (320.0):  70%|███████   | 14/20 [00:54<00:57,  9.53s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What will happen to VUECIN’s existing debt instruments as a result of the new term loan?
Predicted Answer: VUECIN's existing debt instruments, including senior secured floating rate notes in Euro and senior secured notes in sterling, will be entirely taken out and refinanced with the proceeds of the new term loan.


Average Metric: 48.800000000000004 / 15  (325.3):  75%|███████▌  | 15/20 [01:08<00:54, 10.87s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: How will the new debt issuance affect the deliverables into VUECIN’s CDS contracts?
Predicted Answer: The new debt issuance will result in only loans being deliverable into VUECIN's CDS contracts.


Average Metric: 52.400000000000006 / 16  (327.5):  80%|████████  | 16/20 [01:21<00:46, 11.56s/it]

Faithful: 5
Detail: 3
Overall: 5
Test Question: What was the impact on VUECIN’s leverage due to the new financing?
Predicted Answer: The new financing did not impact VUECIN's leverage, which is expected to remain flat at 5.5x.


Average Metric: 56.00000000000001 / 17  (329.4):  85%|████████▌ | 17/20 [01:37<00:38, 12.86s/it]

Faithful: 5
Detail: 3
Overall: 5
Test Question: Why is BATSLN particularly vulnerable to the FDA’s potential regulation on menthol cigarettes?
Predicted Answer: BATSLN is particularly vulnerable to the FDA's potential regulation on menthol cigarettes because it produces Newport, the best-selling brand of menthol cigarettes in the US, which represent approximately ¼ of BATSLN's profit.


Average Metric: 60.00000000000001 / 18  (333.3):  90%|█████████ | 18/20 [06:40<03:20, 100.07s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: How did BATSLN's stock respond to the news of potential FDA restrictions on menthol cigarettes?
Predicted Answer: BATSLN's stock fell by 11% after the news of potential FDA restrictions on menthol cigarettes.


Average Metric: 64.0 / 19  (336.8):  95%|█████████▌| 19/20 [06:50<01:12, 72.92s/it] 

Faithful: 5
Detail: 5
Overall: 5
Test Question: What other factor has contributed to the increased caution from investors towards BATSLN?
Predicted Answer: The potential restrictions on menthol in cigarettes, impacting BATSLN's production of menthol cigarettes, have contributed to the increased caution from investors towards BATSLN.


Average Metric: 68.0 / 20  (340.0): 100%|██████████| 20/20 [07:03<00:00, 21.16s/it]

Faithful: 5
Detail: 5
Overall: 5
Average Metric: 68.0 / 20  (340.0%)

	question	answer	llm_metric
0	How did Alcoa Inc revise its market deficit projection for 2016?	Alcoa Inc revised its market deficit projection for 2016 to a 1.1 million metric tons deficit.	3.6
1	What significant financial move did ArcelorMittal SA announce?	ArcelorMittal SA announced a $3 billion capital increase alongside the sale of a $1 billion stake in Spanish auto-parts maker Gestamp.	3.8
2	At what levels did ArcelorMittal SA propose to buy back their bonds, and how was this received?	ArcelorMittal SA proposed to buy back their bonds at levels substantially above where they were trading in the secondary market, and this was positively received...	4.0
3	How did investors react to ArcelorMittal's bond buyback announcement?	Investors reacted positively to ArcelorMittal's bond buyback announcement, sending the company's 5-year CDS to its tightest level since the March roll.	4.0
4	Who owns and manages OTE (Hellenic Telecommunications Organisation SA)?	OTE (Hellenic Telecommunications Organisation SA) is owned and managed by Germany's DT (Deutsche Telekom AG).	3.6

... 15 more rows not displayed ...

340.0

Finally, BayesianSignatureOptimizer helps beat the RAG baseline, but by not much. Cannot really say it is statistically significant…

evaluate(third_compiled_rag, metric=llm_metric)

Average Metric: 4.0 / 1  (400.0):   5%|▌         | 1/20 [00:00<00:07,  2.42it/s]

Test Question: How did Alcoa Inc revise its market deficit projection for 2016?
Predicted Answer: Alcoa Inc revised its market deficit projection for 2016 from 1.2 million metric tons to 1.1 million metric tons as a result of slowing Chinese demand.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 8.0 / 2  (400.0):  10%|█         | 2/20 [00:00<00:06,  2.60it/s]

Test Question: What significant financial move did ArcelorMittal SA announce?
Predicted Answer: ArcelorMittal SA announced a $3 billion capital increase alongside the sale of a $1 billion stake in Spanish auto-parts maker Gestamp. This move aimed to reduce its debt pile by $4 billion to below $12 billion from the current $15.7 billion. This significant financial move added to the positive momentum in the market and led to a tightening of its 5-year risk premium by 141bps to 836bps.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 12.0 / 3  (400.0):  15%|█▌        | 3/20 [00:01<00:06,  2.71it/s]

Test Question: At what levels did ArcelorMittal SA propose to buy back their bonds, and how was this received?
Predicted Answer: ArcelorMittal SA proposed to buy back €1 billion of notes maturing in November 2017 and €500 million due in March 2018, as well as offering to buy back $1.5 billion of securities maturing in June 2018. The levels at which ArcelorMittal SA planned to buy their bonds were substantially above the levels at which they were trading in the secondary market. Despite this, investors took the proposal positively, and the 5-year CDS tightened to its tightest level (584bps) since the March roll, allowing ArcelorMittal SA to resume its impressive march tighter since the beginning of the year.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 16.0 / 4  (400.0):  20%|██        | 4/20 [00:01<00:08,  1.87it/s]

Test Question: How did investors react to ArcelorMittal's bond buyback announcement?
Predicted Answer: Investors reacted positively to ArcelorMittal's bond buyback announcement, sending the 5-year CDS at its tightest level (584bps) since the March roll. This enabled ArcelorMittal to resume its impressive march tighter since the beginning of the year.
Faithful: 5
Detail: 5
Overall: 5


Average Metric: 19.6 / 5  (392.0):  25%|██▌       | 5/20 [00:02<00:06,  2.15it/s]

Test Question: Who owns and manages OTE (Hellenic Telecommunications Organisation SA)?
Predicted Answer: OTE (Hellenic Telecommunications Organisation SA) is 40% owned and managed by Germany's DT (Deutsche Telekom AG).
Faithful: 5
Detail: 3
Overall: 5


Average Metric: 23.200000000000003 / 6  (386.7):  30%|███       | 6/20 [00:02<00:06,  2.26it/s]

Test Question: What event influenced investor sentiment positively despite OTE's earnings?
Predicted Answer: The positive tone at the Eurogroup meeting, where debt relief options for Greece were discussed earlier than expected, influenced investor sentiment positively despite OTE's earnings. This development raised hopes that a conclusion could be reached in May, leading to a 35bps tightening of OTE's 5-year risk premium at 431bps.
Faithful: 4
Detail: 5
Overall: 5
Test Question: How did the positive developments at the Eurogroup meeting affect OTE’s financial market indicators?
Predicted Answer: The positive tone at the Eurogroup meeting, where debt relief options for Greece were discussed earlier than expected, led to hopes of a conclusion in May. This positive development caused OTE's 5-year risk premium to tighten by 35bps to 431bps.


Average Metric: 27.200000000000003 / 7  (388.6):  35%|███▌      | 7/20 [00:03<00:05,  2.35it/s]

Faithful: 5
Detail: 5
Overall: 5


Average Metric: 28.800000000000004 / 8  (360.0):  35%|███▌      | 7/20 [00:03<00:05,  2.35it/s]

Test Question: What allegations are connected to the investigation surrounding Ohio House Bill 6?
Predicted Answer: The investigation surrounding Ohio House Bill 6 involves allegations of tampering with the slaughter date of poultry being processed at sites owned by 2 Sisters Food Group (2SFG). These allegations include supplying retailers like Tesco, Sainsbury’s, Marks & Spencer, Aldi, and Lidl with poultry products with tampered slaughter dates. Retailers have launched inquiries and suspended their relationships with the firm until the investigation is concluded.
Faithful: 1
Detail: 5
Overall: 1


Average Metric: 30.600000000000005 / 9  (340.0):  45%|████▌     | 9/20 [00:03<00:04,  2.56it/s]

Test Question: What were the consequences for FirstEnergy's leadership following the federal corruption scandal?
Predicted Answer: The consequences for FirstEnergy's leadership following the federal corruption scandal were not explicitly mentioned in the provided context. The focus was on other companies like Anadarko Petroleum Corporation, General Electric Company, Glencore Plc, Toshiba Corp, and Centrica Plc.
Faithful: 1
Detail: 2
Overall: 5
Test Question: What financial operations were highlighted for FirstEnergy in 2019?
Predicted Answer: The context provided does not mention any financial operations or highlights for FirstEnergy in 2019.


Average Metric: 34.00000000000001 / 10  (340.0):  50%|█████     | 10/20 [00:04<00:04,  2.28it/s]

Faithful: 5
Detail: 2
Overall: 5
Test Question: How did the market react to Novafives' third-quarter performance and outlook?
Predicted Answer: The market reacted negatively to Novafives' weak third-quarter results and revised outlook. The company reported weak third-quarter results, with covenant net leverage increasing to 5.8 times from 4.8 in the second quarter. Additionally, the management revised down the Ebitda margin guidance for 2018 to 5%, causing the company's bonds to be punished and indicated approximately 6 points lower on the day. This negative reaction highlighted the challenges Novafives was facing in the market.


Average Metric: 38.00000000000001 / 11  (345.5):  55%|█████▌    | 11/20 [00:20<00:46,  5.20s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What specific issues did Novafives report in their third-quarter results?
Predicted Answer: Novafives reported weak third-quarter results, with covenant net leverage increasing from 4.8 times to 5.8 times. The management revised down the Ebitda margin guidance for 2018 to 5%, while maintaining full-year sales at €2 billion. These issues led to a significant decline in the company's bonds, indicating approximately 6 points lower on the day.


Average Metric: 42.00000000000001 / 12  (350.0):  60%|██████    | 12/20 [00:35<01:05,  8.14s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What financial action did Vue International (VUECIN) take as December approaches?
Predicted Answer: Vue International (VUECIN) announced the issuance of a 7-year facility consisting of two tranches, one in Euro amounting to approximately €600 million and one in GBP amounting to £300 million. The proceeds from this term loan will be used to finance the acquisition of Cinestar, new site capital expenditures, and to refinance existing debt. Additionally, VueCin's existing senior secured floating rate notes in Euro and senior secured notes in sterling will be entirely taken out. This move is expected to keep leverage flat at 5.5x, and as a result, VueCin's 5-year risk premium tightened by 56bps to 296bps.

---


Average Metric: 46.00000000000001 / 13  (353.8):  65%|██████▌   | 13/20 [00:52<01:15, 10.77s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What are the intended uses of the proceeds from Vue International's new term loan?
Predicted Answer: The proceeds from Vue International's new term loan will be used to finance the acquisition of Cinestar and its new site capital expenditures, as well as to refinance existing debt. The existing senior secured floating rate notes in Euro and senior secured notes in sterling will be entirely taken out.


Average Metric: 50.00000000000001 / 14  (357.1):  70%|███████   | 14/20 [01:04<01:08, 11.38s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What will happen to VUECIN’s existing debt instruments as a result of the new term loan?
Predicted Answer: VUECIN's existing senior secured floating rate notes in Euro and senior secured notes in sterling will be entirely taken out as a result of the new term loan. This means that only loans will be deliverable into CDS contracts going forward.


Average Metric: 54.00000000000001 / 15  (360.0):  75%|███████▌  | 15/20 [01:15<00:56, 11.29s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: How will the new debt issuance affect the deliverables into VUECIN’s CDS contracts?
Predicted Answer: The new debt issuance by VUECIN will be used to finance the acquisition of Cinestar, new site capital expenditures, and to refinance existing debt. The existing senior secured floating rate notes in Euro and senior secured notes in sterling will be entirely taken out, meaning that only loans will be deliverable into CDS contracts going forward.


Average Metric: 58.00000000000001 / 16  (362.5):  80%|████████  | 16/20 [01:31<00:50, 12.73s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What was the impact on VUECIN’s leverage due to the new financing?
Predicted Answer: The impact on VUECIN's leverage due to the new financing is expected to be flat at 5.5x.


Average Metric: 61.60000000000001 / 17  (362.4):  85%|████████▌ | 17/20 [01:46<00:39, 13.22s/it]

Faithful: 5
Detail: 3
Overall: 5
Test Question: Why is BATSLN particularly vulnerable to the FDA’s potential regulation on menthol cigarettes?
Predicted Answer: BATSLN is particularly vulnerable to the FDA's potential regulation on menthol cigarettes because it produces Newport, the best-selling brand of menthol cigarettes in the US. Menthol cigarettes represent approximately a quarter of BATSLN's profit, making it the most vulnerable group to any potential regulatory move by the FDA.


Average Metric: 65.60000000000001 / 18  (364.4):  90%|█████████ | 18/20 [01:58<00:25, 12.93s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: How did BATSLN's stock respond to the news of potential FDA restrictions on menthol cigarettes?
Predicted Answer: BATSLN's stock widened by approximately 25bps in response to the news of potential FDA restrictions on menthol cigarettes, as Newport, their best-selling brand in the US, could be affected by the regulatory changes.


Average Metric: 69.60000000000001 / 19  (366.3):  95%|█████████▌| 19/20 [02:13<00:13, 13.62s/it]

Faithful: 5
Detail: 5
Overall: 5
Test Question: What other factor has contributed to the increased caution from investors towards BATSLN?
Predicted Answer: The increased caution from investors towards BATSLN is also due to the higher leverage resulting from the 2017 merger with RAI (Reynolds American Inc), which has raised concerns among investors about the company's financial position and risk profile.


Average Metric: 73.60000000000001 / 20  (368.0): 100%|██████████| 20/20 [02:25<00:00,  7.25s/it]

Faithful: 5
Detail: 5
Overall: 5
Average Metric: 73.60000000000001 / 20  (368.0%)

	question	answer	llm_metric
0	How did Alcoa Inc revise its market deficit projection for 2016?	Alcoa Inc revised its market deficit projection for 2016 from 1.2 million metric tons to 1.1 million metric tons as a result of slowing Chinese...	4.0
1	What significant financial move did ArcelorMittal SA announce?	ArcelorMittal SA announced a $3 billion capital increase alongside the sale of a $1 billion stake in Spanish auto-parts maker Gestamp. This move aimed to...	4.0
2	At what levels did ArcelorMittal SA propose to buy back their bonds, and how was this received?	ArcelorMittal SA proposed to buy back €1 billion of notes maturing in November 2017 and €500 million due in March 2018, as well as offering...	4.0
3	How did investors react to ArcelorMittal's bond buyback announcement?	Investors reacted positively to ArcelorMittal's bond buyback announcement, sending the 5-year CDS at its tightest level (584bps) since the March roll. This enabled ArcelorMittal to...	4.0
4	Who owns and manages OTE (Hellenic Telecommunications Organisation SA)?	OTE (Hellenic Telecommunications Organisation SA) is 40% owned and managed by Germany's DT (Deutsche Telekom AG).	3.6

... 15 more rows not displayed ...

368.0

Conclusion: DSPy contains very interesting “meta” ideas, and brings back the (train, valid, test) sets paradigm to the design of LLM systems, which otherwise are tuned by ad hoc trials and errors attempts.

As future next steps, I will

augment the (train, valid, test) sets;
get more familiar with the other available optimizers;
build a more complex RAG;
implement other DSPy LLM-based systems (sentiment? NER? mapping?).

Prompting is Programming with LMQL

Sun, 11 Feb 2024 00:00:01 +0000

Prompting is Programming with LMQL

In this blog, I just toy around with a relatively new framework for querying (large) language models: LMQL, a SQL-like for LLMs. It is a first step toward a novel programming paradigm: Language Model Programming (LMP). These ideas are described in the very interesting paper Prompting Is Programming: A Query Language for Large Language Models.

From time to time, Machine Learners revisit the concept of what “programming” is. In 2017, Andrej Karpathy with Software 2.0, essentially replacing standard human-written code by a (inputs, neural network, labeled outputs) training + inference procedure. Approach also summarized by his Tweet “Gradient descent can write code better than you. I’m sorry.” In this paradigm,

“software development” takes the form of curating, growing, massaging and cleaning labeled datasets.

As of now, this paradigm is not universally adopted, and standard human-written code still rules for many applications.

In Prompting Is Programming: A Query Language for Large Language Models (2023), authors propose the “Language Model Programming (LMP)” paradigm, where one can program using a mix of text prompting (natural language) and scripting with basic constraints and control flow.

LMQL follows the standard “SELECT FROM WHERE” query pattern from SQL as illustrated on the example below:

In the rest of this post, I will run a couple of LMQL queries to extract some information from market comments which can be found and read on the DataGrapple - Blog.

I used the same source of market comments to experiment with several interesting tools in the past, namely:

Snorkel (01/05/2019) - an instance of Software 2.0
VADER (04/05/2019) - mostly because ‘May the Fourth’
LIME (26/05/2019) - A tool for model-agnostic explanations of Machine Learning models
LIME on Snorkel Labeler (04/08/2019) - Explaining the Snorkel Labeler
SetFit (11/03/2023) - Fine-tuning pretrained language models with limited labeled data

You may want to check them out!

And now, some code:

import os
from pprint import pprint
import lmql

# Set the environment variable
os.environ['OPENAI_API_KEY'] = "YOUR_KEY"

Using LMQL, let’s try to extract from a given market comment:

1) the company name,

2) a credit outlook for the company for 2024,

3) the main risk factor mentioned,

and return the result as a valid python dictionary.

@lmql.query()
async def credit_outlook(market_comment):
  '''lmql
  # contextualize
  "You are a credit analysis working at a prestigious hedge fund for a very busy portfolio manager."
  "You read the following market comment: {market_comment}, and you prepare a crisp structured summary for the portfolio manager. This summary should contain the company name, the credit outlook, and the main risk factor to monitor going forward."

  # provide the company name
  "The name of the company which is the main focus of this market comment is:[COMPANY_NAME]" where STOPS_AT(COMPANY_NAME, "\n")

  # provide the credit outlook
  "Based on this market comment only, the overall credit outlook for this company in 2024 can be considered to be[CLS], because[ANALYSIS]" where CLS in [" positive", " neutral", " negative"]

  # provide the main risk factor
  "Based on this market comment only, the largest potential risk for the company (summarized in at most 3 words) that investors should pay particular attention to for the coming year (2024) is:[RISK], because[RISK_ANALYSIS]"

  return {
    "company_name": COMPANY_NAME.rstrip('\n').lstrip(' '),
    "credit_outlook": CLS.lstrip(' '),
    "main_risk_factor": RISK.lstrip(' '),
  }
  '''

market_comment = "Named after “nature” in Hebrew, Teva Pharmaceutical Industries Ltd [TEVA] is the largest generics lab and stands in the top 20 of global pharmas. Headquartered in Israel, Teva grew by acquisitions from 2000, and even launched a $40B bid for Mylan [2015]. 2017 was a pivotal year, as it saw Teva sell assets, slash divvy -75%, cut staff -25%, and market cap went from $60B+ [2016] to almost $10B. For CDS, this turning point saw a floor at 150bp. While today we approach this floor again, some analysts have argued that 2023 was another pivotal year. Indeed, during 2023, Teva managed to “resolve” [$4.25B] the opioids case, “resolve” [$225m] price-fixing case, continued to reduce net debt, got a new CEO and presented a new strategic plan for the coming years, and returned to revenue growth [1st revenue growth in 6 years]. At its May 2023 investor day, TEVA had said they expect to get back to IG over next 2-3 years, from current BB-/BB-/Ba2. TEVA just reported Q4 EBITDA $1,660m [est $1,374m] although there were chunky one-off upfront payments of $430m on a collaboration with Sanofi. Including these, TEVA ended the year with a net leverage 3.5x. Late last month [Jan 31], TEVA announced its intention to divest its API [active pharmaceutical ingredients] business TAPI, expected done by H1-25, this could take net leverage close but above 3x. Going forward, TEVA guided for FCF $1.7-2.0B this year [EBITDA $4.5-5.0B]. Bloomberg consensus seems more optimistic, with 2.3B penciled in, for both this year and next. Just like that. Now, TEVA still has a decent $20B+ of debt outstanding, ’24 and ’25 maturities seem within anticipated FCF, but the interesting one may be ’26 [$3.4B bonds maturing that year]."

pprint(market_comment)

('Named after “nature” in Hebrew, Teva Pharmaceutical Industries Ltd [TEVA] is '
 'the largest generics lab and stands in the top 20 of global pharmas. '
 'Headquartered in Israel, Teva grew by acquisitions from 2000, and even '
 'launched a $40B bid for Mylan [2015]. 2017 was a pivotal year, as it saw '
 'Teva sell assets, slash divvy -75%, cut staff -25%, and market cap went from '
 '$60B+ [2016] to almost $10B. For CDS, this turning point saw a floor at '
 '150bp. While today we approach this floor again, some analysts have argued '
 'that 2023 was another pivotal year. Indeed, during 2023, Teva managed to '
 '“resolve” [$4.25B] the opioids case, “resolve” [$225m] price-fixing case, '
 'continued to reduce net debt, got a new CEO and presented a new strategic '
 'plan for the coming years, and returned to revenue growth [1st revenue '
 'growth in 6 years]. At its May 2023 investor day, TEVA had said they expect '
 'to get back to IG over next 2-3 years, from current BB-/BB-/Ba2. TEVA just '
 'reported Q4 EBITDA $1,660m [est $1,374m] although there were chunky one-off '
 'upfront payments of $430m on a collaboration with Sanofi. Including these, '
 'TEVA ended the year with a net leverage 3.5x. Late last month [Jan 31], TEVA '
 'announced its intention to divest its API [active pharmaceutical '
 'ingredients] business TAPI, expected done by H1-25, this could take net '
 'leverage close but above 3x. Going forward, TEVA guided for FCF $1.7-2.0B '
 'this year [EBITDA $4.5-5.0B]. Bloomberg consensus seems more optimistic, '
 'with 2.3B penciled in, for both this year and next. Just like that. Now, '
 'TEVA still has a decent $20B+ of debt outstanding, ’24 and ’25 maturities '
 'seem within anticipated FCF, but the interesting one may be ’26 [$3.4B bonds '
 'maturing that year].')

result = await credit_outlook(market_comment)

pprint(result)

{'company_name': 'Teva Pharmaceutical Industries Ltd [TEVA].',
 'credit_outlook': 'positive',
 'main_risk_factor': 'Divestment, Debt Maturities.'}

market_comment = "As usual, Swedish companies reported ahead of most Europeans. Telefonaktiebolaget LM Ericsson [ERICB - Ba1 / BBB- / BBB-] is the company that has invented Bluetooth technology, they are one of the world’s biggest providers of 5G networking equipment, they own c60k patents. Ericsson CDS is a constituent of XOver since series s27. It was in the Main index before that, since series s16. Last month, Ericsson 5-year CDS widened +6bp on earnings day, which extended a series of 8 consecutive widenings on quarterly earnings day. This move was little however, if compared to the average +16bp move in the previous 7 releases. The market surely got accustomed to these downbeat releases [and to the rallies in between these releases], allowing Ericsson 5-year CDS to remain close to one-year tight, 120bp handle. Ericsson CEO warned that the market [outside of China] will continue to decline in 2024. In 2024, Ericsson expects a continued drop in mobile business but is ready for a turnaround. Q4 revenues fell by -17%, mainly due to a -23% decline in NA Networks. While India still contributed to growth, it slowed down notably. Ericsson predicts a global -4% decline in RAN market, offset by cloud software and services & enterprise. FCF decreased on lower EBIT, restructuring charges, and WC. Ericsson ended 2023 with SEK7.8b net cash, down from SEK23.3b at the end of 2022. 2024 is still young but it sounds like another sluggish year, with subdued spending from mobile service providers who have been slower than anticipated to roll out 5G. Longer term, Ericsson’s duopoly also risks being disrupted by ORAN [open radio access networks - operators can choose antennas/infrastructure vendors]. Despite all that, Ericsson confirmed SEK2.70/sh divvy. On the bright side of things, EBIT margin before restructuring charges has surpassed 10%, enabling management to maintain mid-term margin targets: EBITA [15-18%] FCF [9-12%]. Moreover, progress was made on the SEK12b cost savings plan, with 50% done in 2023. Finally, the $14B AT&T deal is hoped to increase revenues in H2. The latter points talk in favor of Ericsson, relative to its peer Nokia, which trades almost 20bp tighter."

pprint(market_comment)

('As usual, Swedish companies reported ahead of most Europeans. '
 'Telefonaktiebolaget LM Ericsson [ERICB - Ba1 / BBB- / BBB-] is the company '
 'that has invented Bluetooth technology, they are one of the world’s biggest '
 'providers of 5G networking equipment, they own c60k patents. Ericsson CDS is '
 'a constituent of XOver since series s27. It was in the Main index before '
 'that, since series s16. Last month, Ericsson 5-year CDS widened +6bp on '
 'earnings day, which extended a series of 8 consecutive widenings on '
 'quarterly earnings day. This move was little however, if compared to the '
 'average +16bp move in the previous 7 releases. The market surely got '
 'accustomed to these downbeat releases [and to the rallies in between these '
 'releases], allowing Ericsson 5-year CDS to remain close to one-year tight, '
 '120bp handle. Ericsson CEO warned that the market [outside of China] will '
 'continue to decline in 2024. In 2024, Ericsson expects a continued drop in '
 'mobile business but is ready for a turnaround. Q4 revenues fell by -17%, '
 'mainly due to a -23% decline in NA Networks. While India still contributed '
 'to growth, it slowed down notably. Ericsson predicts a global -4% decline in '
 'RAN market, offset by cloud software and services & enterprise. FCF '
 'decreased on lower EBIT, restructuring charges, and WC. Ericsson ended 2023 '
 'with SEK7.8b net cash, down from SEK23.3b at the end of 2022. 2024 is still '
 'young but it sounds like another sluggish year, with subdued spending from '
 'mobile service providers who have been slower than anticipated to roll out '
 '5G. Longer term, Ericsson’s duopoly also risks being disrupted by ORAN [open '
 'radio access networks - operators can choose antennas/infrastructure '
 'vendors]. Despite all that, Ericsson confirmed SEK2.70/sh divvy. On the '
 'bright side of things, EBIT margin before restructuring charges has '
 'surpassed 10%, enabling management to maintain mid-term margin targets: '
 'EBITA [15-18%] FCF [9-12%]. Moreover, progress was made on the SEK12b cost '
 'savings plan, with 50% done in 2023. Finally, the $14B AT&T deal is hoped to '
 'increase revenues in H2. The latter points talk in favor of Ericsson, '
 'relative to its peer Nokia, which trades almost 20bp tighter.')

result = await credit_outlook(market_comment)

pprint(result)

{'company_name': 'Telefonaktiebolaget LM Ericsson.',
 'credit_outlook': 'negative',
 'main_risk_factor': 'ORAN disruption.'}

market_comment = "Intrum is a Sweden-based credit management services company with subsidiaries in Europe. They offer services covering the entire credit management chain, ranging from prospecting and segmenting to payment management services, collection and payment services [sales ledger, reminder, e-commerce, VAT refunds, debt collection, debt surveillance]. Intrum CDS is a constituent of XOver since s28, currently rated B2/BB/BB-. Last summer, Intrum reported available liquidity SEK 13b and SEK 61.3b total debt [bonds CPs PPs RCF] and was planning to cut leverage by scrapping 2024 dividend and exiting 3 additional markets completely [Czech Republic, Slovakia and Hungary]. This week, not only did we learn that the Eastern Europe exit was not guaranteed any longer either, but Intrum also agreed to sell part of its portfolio, to raise SEK8.2b [by H1 end]. This confirms rumours from December, and covers slightly more than a third of debt due by 2025 end. So, Intrum sold two thirds what could be their crown jewel assets. The assets they could sell, anyway. For 98% of Sept 30th book value. Retaining a 35% stake in the entity acquiring them. Cerberus owning the other 65%. And Intrum agreed to keep servicing the portfolio for at least 5 years. Even before the call Tuesday morning, this benefitted the very short end of the curve, but was already slight negative for the longer end and for the equity [FCF dilutive, increasing leverage]. While Tuesday morning started around 13.5% upfront [5-year / before the call], Intrum CDS had reached 18% upfront by noon, and widened a total +8pt over two sessions. Skepticism about achieving servicing targets, reduced RCF capacity, limited asset sales prospects going forward, are likely to keep Intrum CDS in the top 3 of widest names in crossover benchmark. Its now reverted tighter -2.5pt over the past two sessions, in a slight reversal. A successful RCF maturity extension is what could take it back further."

pprint(market_comment)

('Intrum is a Sweden-based credit management services company with '
 'subsidiaries in Europe. They offer services covering the entire credit '
 'management chain, ranging from prospecting and segmenting to payment '
 'management services, collection and payment services [sales ledger, '
 'reminder, e-commerce, VAT refunds, debt collection, debt surveillance]. '
 'Intrum CDS is a constituent of XOver since s28, currently rated B2/BB/BB-. '
 'Last summer, Intrum reported available liquidity SEK 13b and SEK 61.3b total '
 'debt [bonds CPs PPs RCF] and was planning to cut leverage by scrapping 2024 '
 'dividend and exiting 3 additional markets completely [Czech Republic, '
 'Slovakia and Hungary]. This week, not only did we learn that the Eastern '
 'Europe exit was not guaranteed any longer either, but Intrum also agreed to '
 'sell part of its portfolio, to raise SEK8.2b [by H1 end]. This confirms '
 'rumours from December, and covers slightly more than a third of debt due by '
 '2025 end. So, Intrum sold two thirds what could be their crown jewel assets. '
 'The assets they could sell, anyway. For 98% of Sept 30th book value. '
 'Retaining a 35% stake in the entity acquiring them. Cerberus owning the '
 'other 65%. And Intrum agreed to keep servicing the portfolio for at least 5 '
 'years. Even before the call Tuesday morning, this benefitted the very short '
 'end of the curve, but was already slight negative for the longer end and for '
 'the equity [FCF dilutive, increasing leverage]. While Tuesday morning '
 'started around 13.5% upfront [5-year / before the call], Intrum CDS had '
 'reached 18% upfront by noon, and widened a total +8pt over two sessions. '
 'Skepticism about achieving servicing targets, reduced RCF capacity, limited '
 'asset sales prospects going forward, are likely to keep Intrum CDS in the '
 'top 3 of widest names in crossover benchmark. Its now reverted tighter '
 '-2.5pt over the past two sessions, in a slight reversal. A successful RCF '
 'maturity extension is what could take it back further.')

result = await credit_outlook(market_comment)

pprint(result)

{'company_name': 'Intrum',
 'credit_outlook': 'negative',
 'main_risk_factor': 'RCF maturity extension.'}

market_comment = "CMA CGM SA [CMACG] is the #3 global shipping co. The services they provide include vessel/container fleet management, freight delivery, logistics, cargo cruises. CMA CGM is privately controlled by the Saade family [founders]. CMA CGM is HQ’d in Marseille and sponsors the [un]beloved OM football club. CMA CGM CDS has been in CrossOver indices from s25 to s38. After reaching 50% upfront at the peak of the covid financial crisis, CMA CGM CDS tightened massively and continuously, with conservative balance-sheet management coupled with surging earnings in the subsequent 2 years, thanks to high freight rates and saturated supply chains. While CMA CGM had less than €1.7B cash at EoY-20, they reported €11.3B cash at EoY-22. Jan 30 last year, CMA-CGM 5-year CDS spread squeezed tighter -110bp to 285bp, after reports that they’d redeem €525M 7.5% 26s at 103.75. Some analysts at the time noted CMACG CDS might find itself without any deliverable obligation, temporarily orphan. Since then, CMA CGM has not come back to the market, but still guarantees c€100m Fenix Marine debt, c€80m first lien secured loans at SCI Tour d’Arenc, on top of the €200m+ first lien secured loans at CMA CGM SA. Last May, CMA CGM Group granted a put option to the Bolloré Group to sell the transport and logistics operations of Bolloré Logistics. CMA CGM then started reporting significantly lower net income [$2B Q1-23 from $7B+ Q1-22] marking the end of two golden years. And last July, the Bolloré Group announced that it signed the share purchase agreement to sell 100% of Bolloré Logistics to the CMA CGM Group, purchase price €4.65B ex net debt, completion subject to antitrust and foreign investment clearances. In much smaller acquisitions, the Saade family has taken a 10% stake in French broadcaster M6, a stake in online media Brut and bought newspaper La Tribune, regional newspaper La Provence. June last year, Moody’s took CMA CGM's long-term corporate family rating up one notch to Ba1, with a stable outlook [from positive], praising continued high FCF generation used to fund both logistics and terminals acquisitions, as well as debt reduction, strengthening B/S. During H2-23 and to date, the open interest reported at clearing houses on CMA CGM CDS, has been about halved, now just shy of €400m across Ice Clear and LCH, and across all maturities, highlighting the depth of the short cutting enterprise that’s been taking place, although the fact that this name is not in CrossOver indices since series s38 surely played a role. Aside October-last-year macro weakness, there has been NO exit door for shorts on CMA CGM CDS. More recently, the fact that several shipping companies announced a pause on Red Sea passages [following USUK/Houthi local war -- inducing a c30% significant extension via South Africa] is likely to boost container companies’ earnings prospects. On that, Reuters has reported that CMA CGM’s Asia-Med freight rates had soared by up to 100% [!] as of this past Monday, compared to Monday Jan 1st. Absent a short resolution in the Red Sea, 200bp does not feel like a solid floor."

pprint(market_comment)

('CMA CGM SA [CMACG] is the #3 global shipping co. The services they provide '
 'include vessel/container fleet management, freight delivery, logistics, '
 'cargo cruises. CMA CGM is privately controlled by the Saade family '
 '[founders]. CMA CGM is HQ’d in Marseille and sponsors the [un]beloved OM '
 'football club. CMA CGM CDS has been in CrossOver indices from s25 to s38. '
 'After reaching 50% upfront at the peak of the covid financial crisis, CMA '
 'CGM CDS tightened massively and continuously, with conservative '
 'balance-sheet management coupled with surging earnings in the subsequent 2 '
 'years, thanks to high freight rates and saturated supply chains. While CMA '
 'CGM had less than €1.7B cash at EoY-20, they reported €11.3B cash at EoY-22. '
 'Jan 30 last year, CMA-CGM 5-year CDS spread squeezed tighter -110bp to '
 '285bp, after reports that they’d redeem €525M 7.5% 26s at 103.75. Some '
 'analysts at the time noted CMACG CDS might find itself without any '
 'deliverable obligation, temporarily orphan. Since then, CMA CGM has not come '
 'back to the market, but still guarantees c€100m Fenix Marine debt, c€80m '
 'first lien secured loans at SCI Tour d’Arenc, on top of the €200m+ first '
 'lien secured loans at CMA CGM SA. Last May, CMA CGM Group granted a put '
 'option to the Bolloré Group to sell the transport and logistics operations '
 'of Bolloré Logistics. CMA CGM then started reporting significantly lower net '
 'income [$2B Q1-23 from $7B+ Q1-22] marking the end of two golden years. And '
 'last July, the Bolloré Group announced that it signed the share purchase '
 'agreement to sell 100% of Bolloré Logistics to the CMA CGM Group, purchase '
 'price €4.65B ex net debt, completion subject to antitrust and foreign '
 'investment clearances. In much smaller acquisitions, the Saade family has '
 'taken a 10% stake in French broadcaster M6, a stake in online media Brut and '
 'bought newspaper La Tribune, regional newspaper La Provence. June last year, '
 "Moody’s took CMA CGM's long-term corporate family rating up one notch to "
 'Ba1, with a stable outlook [from positive], praising continued high FCF '
 'generation used to fund both logistics and terminals acquisitions, as well '
 'as debt reduction, strengthening B/S. During H2-23 and to date, the open '
 'interest reported at clearing houses on CMA CGM CDS, has been about halved, '
 'now just shy of €400m across Ice Clear and LCH, and across all maturities, '
 'highlighting the depth of the short cutting enterprise that’s been taking '
 'place, although the fact that this name is not in CrossOver indices since '
 'series s38 surely played a role. Aside October-last-year macro weakness, '
 'there has been NO exit door for shorts on CMA CGM CDS. More recently, the '
 'fact that several shipping companies announced a pause on Red Sea passages '
 '[following USUK/Houthi local war -- inducing a c30% significant extension '
 'via South Africa] is likely to boost container companies’ earnings '
 'prospects. On that, Reuters has reported that CMA CGM’s Asia-Med freight '
 'rates had soared by up to 100% [!] as of this past Monday, compared to '
 'Monday Jan 1st. Absent a short resolution in the Red Sea, 200bp does not '
 'feel like a solid floor.')

result = await credit_outlook(market_comment)

pprint(result)

{'company_name': 'CMA CGM SA.',
 'credit_outlook': 'positive',
 'main_risk_factor': 'Red Sea conflict.'}

market_comment = "Founded in 1909 as a small family-run clinical analysis laboratory in Barcelona, Grifols S.A. is now a global pharma/chemical manufacturer, European leader as producer of blood plasma-based products. Grifols also supply devices, instruments, and reagents for clinical testing laboratories. Rated Caa1/B-/B+ Grifols has its CDS in all CrossOver indices since vintage s28 included. COVID had been the primary driver of reduced plasma volumes, and started a new era for Grifols CDS. EBITDA margin remained strong 25% [20] 20% [21] but COVID net impact on EBITDA was -€500m [lower plasma volumes -€420m] and net leverage increased in 2021 off strategic acquisitions [€520m]. Now valued €5.5B in the stock market, Grifols had reported €10B long term debt as of 2022 end. They have two outstanding bonds totaling €2B due 2025, the next major bonds maturity year being 2027 [roughly another €2B]. In June 2022, Grifols outperformed after a weak H1 [300bp -> 600bp] and Grifols stocks fell, as they were reported to be in talks to raise €2B equity. This was seen as showing a shift in Grifols family vision about leverage and control. However this was after leverage reduction had been guided in February 2022. At the time net debt €5.8B [end 2021] net leverage 5.4x, liquidity €1.3B cash €0.7B. Last June 14, Grifols 5-year CDS tightened -70bp to 430bp, as Grifols may sell off assets including the 26% in Shanghai RAAS it bought in 2019. Grifols had entered a strategic alliance with the Chinese group in 2020. Grifols SA said that they would expect to receive $1.5B if a deal goes through, boosting their debt cutting effort. The beautiful rally ended THIS WEEK. On Tuesday [Jan 9] Gotham City Research LLC stated that Grifols manipulates reported debt & EBITDA to artificially reduce reported leverage to 6x, which they believe is closer to 12x. Gotham argued that both Grifols and Scranton Enterprises [a Grifols family vehicle] fully consolidate BPC + Haema onto their financial statements, a deceptive and incorrect treatment. Gotham said that should their estimate of Grifols’ true leverage be correct, the company should face notably higher financing costs, and they believe that Grifols equities are uninvestable, “likely zero”. Gotham said Grifols fully consolidates both Haema and BPC Plasma, despite owning 0% of each company, Haema + BPC are material to Grifols as they account for c40% of Grifols earnings from non-controlling interests, Scranton Enterprises also fully consolidates BPC Plasma and Haema, these entities’ earnings appear to account for >100% of Scranton’s earnings, while Scranton owns 8.4% of Grifols. At a lesser level, Gotham claimed that Grifols lent Scranton $95m in 2018, a loan seeming tied to the BPC/Haema transaction, yet this loan was undisclosed in Grifols’ filings, only appearing in Scranton filings. Scranton’s 2021 filing shows an increase in liabilities of €59m relating to “advance payments from Grifols Worldwide Operations Ltd”, another transaction undisclosed in Grifols filings. Finally, Gotham argued that while Grifols’ CEO is hailed as a change in direction, he has actually been with Grifols since 2006, and was on the board at the time the suspect transactions occurred. Grifols put out a communique commenting upon “recent false information and speculations published by Gotham City Research regarding their accounting and financial disclosure”. In this, Grifols did deny and reject allegations of wrongful accounting or reporting practices of their consolidated financial statements, noting that their annual audits are conducted by one of the big-four audit firms. On Wednesday [Jan 10], the owner of Gotham City Research, General Industrial Partners, reduced its short position in Grifols to 0.06% from 0.6%. Job done. Grifols shares pared their two-day decline to 18%. From 43% early Tuesday. Today [Jan 12], Grifols 5-year CDS widened back roughly 90bp, on concerns about the collapse of a $1.8B deal to sell a stake in Shanghai RAAS Blood Products to a Chinese player named Haier. The market fears a breakdown in the deal due to the turmoil ignited this week by Gotham. However, some other analysts express confidence that the deal w ill be closed in the coming months."

pprint(market_comment)

('Founded in 1909 as a small family-run clinical analysis laboratory in '
 'Barcelona, Grifols S.A. is now a global pharma/chemical manufacturer, '
 'European leader as producer of blood plasma-based products. Grifols also '
 'supply devices, instruments, and reagents for clinical testing laboratories. '
 'Rated Caa1/B-/B+ Grifols has its CDS in all CrossOver indices since vintage '
 's28 included. COVID had been the primary driver of reduced plasma volumes, '
 'and started a new era for Grifols CDS. EBITDA margin remained strong 25% '
 '[20] 20% [21] but COVID net impact on EBITDA was -€500m [lower plasma '
 'volumes -€420m] and net leverage increased in 2021 off strategic '
 'acquisitions [€520m]. Now valued €5.5B in the stock market, Grifols had '
 'reported €10B long term debt as of 2022 end. They have two outstanding bonds '
 'totaling €2B due 2025, the next major bonds maturity year being 2027 '
 '[roughly another €2B]. In June 2022, Grifols outperformed after a weak H1 '
 '[300bp -> 600bp] and Grifols stocks fell, as they were reported to be in '
 'talks to raise €2B equity. This was seen as showing a shift in Grifols '
 'family vision about leverage and control. However this was after leverage '
 'reduction had been guided in February 2022. At the time net debt €5.8B [end '
 '2021] net leverage 5.4x, liquidity €1.3B cash €0.7B. Last June 14, Grifols '
 '5-year CDS tightened -70bp to 430bp, as Grifols may sell off assets '
 'including the 26% in Shanghai RAAS it bought in 2019. Grifols had entered a '
 'strategic alliance with the Chinese group in 2020. Grifols SA said that they '
 'would expect to receive $1.5B if a deal goes through, boosting their debt '
 'cutting effort. The beautiful rally ended THIS WEEK. On Tuesday [Jan 9] '
 'Gotham City Research LLC stated that Grifols manipulates reported debt & '
 'EBITDA to artificially reduce reported leverage to 6x, which they believe is '
 'closer to 12x. Gotham argued that both Grifols and Scranton Enterprises [a '
 'Grifols family vehicle] fully consolidate BPC + Haema onto their financial '
 'statements, a deceptive and incorrect treatment. Gotham said that should '
 'their estimate of Grifols’ true leverage be correct, the company should face '
 'notably higher financing costs, and they believe that Grifols equities are '
 'uninvestable, “likely zero”. Gotham said Grifols fully consolidates both '
 'Haema and BPC Plasma, despite owning 0% of each company, Haema + BPC are '
 'material to Grifols as they account for c40% of Grifols earnings from '
 'non-controlling interests, Scranton Enterprises also fully consolidates BPC '
 'Plasma and Haema, these entities’ earnings appear to account for >100% of '
 'Scranton’s earnings, while Scranton owns 8.4% of Grifols. At a lesser level, '
 'Gotham claimed that Grifols lent Scranton $95m in 2018, a loan seeming tied '
 'to the BPC/Haema transaction, yet this loan was undisclosed in Grifols’ '
 'filings, only appearing in Scranton filings. Scranton’s 2021 filing shows an '
 'increase in liabilities of €59m relating to “advance payments from Grifols '
 'Worldwide Operations Ltd”, another transaction undisclosed in Grifols '
 'filings. Finally, Gotham argued that while Grifols’ CEO is hailed as a '
 'change in direction, he has actually been with Grifols since 2006, and was '
 'on the board at the time the suspect transactions occurred. Grifols put out '
 'a communique commenting upon “recent false information and speculations '
 'published by Gotham City Research regarding their accounting and financial '
 'disclosure”. In this, Grifols did deny and reject allegations of wrongful '
 'accounting or reporting practices of their consolidated financial '
 'statements, noting that their annual audits are conducted by one of the '
 'big-four audit firms. On Wednesday [Jan 10], the owner of Gotham City '
 'Research, General Industrial Partners, reduced its short position in Grifols '
 'to 0.06% from 0.6%. Job done. Grifols shares pared their two-day decline to '
 '18%. From 43% early Tuesday. Today [Jan 12], Grifols 5-year CDS widened back '
 'roughly 90bp, on concerns about the collapse of a $1.8B deal to sell a stake '
 'in Shanghai RAAS Blood Products to a Chinese player named Haier. The market '
 'fears a breakdown in the deal due to the turmoil ignited this week by '
 'Gotham. However, some other analysts express confidence that the deal w ill '
 'be closed in the coming months.')

result = await credit_outlook(market_comment)

pprint(result)

{'company_name': 'Grifols S.A.',
 'credit_outlook': 'negative',
 'main_risk_factor': 'Gotham allegations, deal failure'}

Not bad. Let’s now try something a bit harder but supported by LMQL: a while control flow.

We will try to have LMQL extract {(cause) => (consequence)} that could potentially lead to the default of the company, according to the content of the market comment.

@lmql.query()
async def causal_mechanism_bankruptcy(market_comment):
  '''lmql
    argmax
    "You are a credit analysis working at a prestigious hedge fund for a very busy portfolio manager."
    "You read the following market comment: {market_comment}, and you write for the portfolio manager a crisp causal explanation of the most likely way the company mentioned in the comment could default within the next couple of years."
    "The name of the company which is the main focus of this market comment is:[COMPANY_NAME]" where STOPS_AT(COMPANY_NAME, "\n")
    "Based on this market comment only, the most likely way this company could default within the next couple of years can be explained by the following causal mechanism:[CAUSAL_MECHANISM]"
    "Please, write the explanation of the causal mechanism leading to a potential default of the company (which should be the final consequence and noted 'DEFAULT' in the explanation). The explanation should be written in a simple format such as CAUSE_1 => CONSEQUENCE_1, CAUSE_2 => CONSEQUENCE_2, CAUSE_3 and CONSEQUENCE_2 => DEFAULT. 'DEFAULT' string is the final consequence of the explanation. The state of DEFAULT cannot be a cause, only a consequence. Each CAUSE_i and CONSEQUENCE_i should be short and no more than 5 words long."
    "A list of a cause followed by (and separated by the symbol '=>') its consequence with the format CAUSE_i => CONSEQUENCE_i that can yield to the default of a company):\n"
    causal_list = []
    CONSEQUENCE = ""
    while CONSEQUENCE.lstrip(' ').rstrip('\n') not in ['DEFAULT', 'Potential default']:
      "-[CAUSE] =>[CONSEQUENCE]\n" where STOPS_AT(CAUSE, "=>") and STOPS_AT(CONSEQUENCE, "\n")
      causal_list.append({
        "cause": CAUSE.lstrip(' ').rstrip(' =>'),
        "consequence": CONSEQUENCE.lstrip(' ').rstrip('\n')})
    return {
      "company_name": COMPANY_NAME.rstrip('\n').lstrip(' '),
      "causal_explanation": causal_list,
    }
  '''

market_comment = "Named after “nature” in Hebrew, Teva Pharmaceutical Industries Ltd [TEVA] is the largest generics lab and stands in the top 20 of global pharmas. Headquartered in Israel, Teva grew by acquisitions from 2000, and even launched a $40B bid for Mylan [2015]. 2017 was a pivotal year, as it saw Teva sell assets, slash divvy -75%, cut staff -25%, and market cap went from $60B+ [2016] to almost $10B. For CDS, this turning point saw a floor at 150bp. While today we approach this floor again, some analysts have argued that 2023 was another pivotal year. Indeed, during 2023, Teva managed to “resolve” [$4.25B] the opioids case, “resolve” [$225m] price-fixing case, continued to reduce net debt, got a new CEO and presented a new strategic plan for the coming years, and returned to revenue growth [1st revenue growth in 6 years]. At its May 2023 investor day, TEVA had said they expect to get back to IG over next 2-3 years, from current BB-/BB-/Ba2. TEVA just reported Q4 EBITDA $1,660m [est $1,374m] although there were chunky one-off upfront payments of $430m on a collaboration with Sanofi. Including these, TEVA ended the year with a net leverage 3.5x. Late last month [Jan 31], TEVA announced its intention to divest its API [active pharmaceutical ingredients] business TAPI, expected done by H1-25, this could take net leverage close but above 3x. Going forward, TEVA guided for FCF $1.7-2.0B this year [EBITDA $4.5-5.0B]. Bloomberg consensus seems more optimistic, with 2.3B penciled in, for both this year and next. Just like that. Now, TEVA still has a decent $20B+ of debt outstanding, ’24 and ’25 maturities seem within anticipated FCF, but the interesting one may be ’26 [$3.4B bonds maturing that year]."

pprint(market_comment)

('Named after “nature” in Hebrew, Teva Pharmaceutical Industries Ltd [TEVA] is '
 'the largest generics lab and stands in the top 20 of global pharmas. '
 'Headquartered in Israel, Teva grew by acquisitions from 2000, and even '
 'launched a $40B bid for Mylan [2015]. 2017 was a pivotal year, as it saw '
 'Teva sell assets, slash divvy -75%, cut staff -25%, and market cap went from '
 '$60B+ [2016] to almost $10B. For CDS, this turning point saw a floor at '
 '150bp. While today we approach this floor again, some analysts have argued '
 'that 2023 was another pivotal year. Indeed, during 2023, Teva managed to '
 '“resolve” [$4.25B] the opioids case, “resolve” [$225m] price-fixing case, '
 'continued to reduce net debt, got a new CEO and presented a new strategic '
 'plan for the coming years, and returned to revenue growth [1st revenue '
 'growth in 6 years]. At its May 2023 investor day, TEVA had said they expect '
 'to get back to IG over next 2-3 years, from current BB-/BB-/Ba2. TEVA just '
 'reported Q4 EBITDA $1,660m [est $1,374m] although there were chunky one-off '
 'upfront payments of $430m on a collaboration with Sanofi. Including these, '
 'TEVA ended the year with a net leverage 3.5x. Late last month [Jan 31], TEVA '
 'announced its intention to divest its API [active pharmaceutical '
 'ingredients] business TAPI, expected done by H1-25, this could take net '
 'leverage close but above 3x. Going forward, TEVA guided for FCF $1.7-2.0B '
 'this year [EBITDA $4.5-5.0B]. Bloomberg consensus seems more optimistic, '
 'with 2.3B penciled in, for both this year and next. Just like that. Now, '
 'TEVA still has a decent $20B+ of debt outstanding, ’24 and ’25 maturities '
 'seem within anticipated FCF, but the interesting one may be ’26 [$3.4B bonds '
 'maturing that year].')

result = await causal_mechanism_bankruptcy(market_comment)

pprint(result)

{'causal_explanation': [{'cause': 'Legal issues',
                         'consequence': 'Financial settlements'},
                        {'cause': 'Divestment of API business',
                         'consequence': 'Decrease in revenue/earnings'},
                        {'cause': 'High level of debt',
                         'consequence': 'Difficulty meeting obligations'},
                        {'cause': 'Downgraded credit ratings',
                         'consequence': 'Difficulty refinancing debt'},
                        {'cause': 'Debt maturing in 2026',
                         'consequence': 'Difficulty refinancing debt'},
                        {'cause': 'Inability to regain investment-grade status',
                         'consequence': 'Difficulty refinancing debt'},
                        {'cause': 'Legal issues and divestment of API business',
                         'consequence': 'Decrease in financial stability'},
                        {'cause': 'Decrease in revenue/earnings and '
                                  'lower-than-expected free cash flow',
                         'consequence': 'Difficulty meeting debt obligations'},
                        {'cause': 'Difficulty meeting obligations and '
                                  'difficulty refinancing debt',
                         'consequence': 'DEFAULT'}],
 'company_name': 'Teva Pharmaceutical Industries Ltd [TEVA].'}

market_comment = "As usual, Swedish companies reported ahead of most Europeans. Telefonaktiebolaget LM Ericsson [ERICB - Ba1 / BBB- / BBB-] is the company that has invented Bluetooth technology, they are one of the world’s biggest providers of 5G networking equipment, they own c60k patents. Ericsson CDS is a constituent of XOver since series s27. It was in the Main index before that, since series s16. Last month, Ericsson 5-year CDS widened +6bp on earnings day, which extended a series of 8 consecutive widenings on quarterly earnings day. This move was little however, if compared to the average +16bp move in the previous 7 releases. The market surely got accustomed to these downbeat releases [and to the rallies in between these releases], allowing Ericsson 5-year CDS to remain close to one-year tight, 120bp handle. Ericsson CEO warned that the market [outside of China] will continue to decline in 2024. In 2024, Ericsson expects a continued drop in mobile business but is ready for a turnaround. Q4 revenues fell by -17%, mainly due to a -23% decline in NA Networks. While India still contributed to growth, it slowed down notably. Ericsson predicts a global -4% decline in RAN market, offset by cloud software and services & enterprise. FCF decreased on lower EBIT, restructuring charges, and WC. Ericsson ended 2023 with SEK7.8b net cash, down from SEK23.3b at the end of 2022. 2024 is still young but it sounds like another sluggish year, with subdued spending from mobile service providers who have been slower than anticipated to roll out 5G. Longer term, Ericsson’s duopoly also risks being disrupted by ORAN [open radio access networks - operators can choose antennas/infrastructure vendors]. Despite all that, Ericsson confirmed SEK2.70/sh divvy. On the bright side of things, EBIT margin before restructuring charges has surpassed 10%, enabling management to maintain mid-term margin targets: EBITA [15-18%] FCF [9-12%]. Moreover, progress was made on the SEK12b cost savings plan, with 50% done in 2023. Finally, the $14B AT&T deal is hoped to increase revenues in H2. The latter points talk in favor of Ericsson, relative to its peer Nokia, which trades almost 20bp tighter."

pprint(market_comment)

('As usual, Swedish companies reported ahead of most Europeans. '
 'Telefonaktiebolaget LM Ericsson [ERICB - Ba1 / BBB- / BBB-] is the company '
 'that has invented Bluetooth technology, they are one of the world’s biggest '
 'providers of 5G networking equipment, they own c60k patents. Ericsson CDS is '
 'a constituent of XOver since series s27. It was in the Main index before '
 'that, since series s16. Last month, Ericsson 5-year CDS widened +6bp on '
 'earnings day, which extended a series of 8 consecutive widenings on '
 'quarterly earnings day. This move was little however, if compared to the '
 'average +16bp move in the previous 7 releases. The market surely got '
 'accustomed to these downbeat releases [and to the rallies in between these '
 'releases], allowing Ericsson 5-year CDS to remain close to one-year tight, '
 '120bp handle. Ericsson CEO warned that the market [outside of China] will '
 'continue to decline in 2024. In 2024, Ericsson expects a continued drop in '
 'mobile business but is ready for a turnaround. Q4 revenues fell by -17%, '
 'mainly due to a -23% decline in NA Networks. While India still contributed '
 'to growth, it slowed down notably. Ericsson predicts a global -4% decline in '
 'RAN market, offset by cloud software and services & enterprise. FCF '
 'decreased on lower EBIT, restructuring charges, and WC. Ericsson ended 2023 '
 'with SEK7.8b net cash, down from SEK23.3b at the end of 2022. 2024 is still '
 'young but it sounds like another sluggish year, with subdued spending from '
 'mobile service providers who have been slower than anticipated to roll out '
 '5G. Longer term, Ericsson’s duopoly also risks being disrupted by ORAN [open '
 'radio access networks - operators can choose antennas/infrastructure '
 'vendors]. Despite all that, Ericsson confirmed SEK2.70/sh divvy. On the '
 'bright side of things, EBIT margin before restructuring charges has '
 'surpassed 10%, enabling management to maintain mid-term margin targets: '
 'EBITA [15-18%] FCF [9-12%]. Moreover, progress was made on the SEK12b cost '
 'savings plan, with 50% done in 2023. Finally, the $14B AT&T deal is hoped to '
 'increase revenues in H2. The latter points talk in favor of Ericsson, '
 'relative to its peer Nokia, which trades almost 20bp tighter.')

result = await causal_mechanism_bankruptcy(market_comment)

pprint(result)

{'causal_explanation': [{'cause': 'Continued decline in global RAN market',
                         'consequence': 'Decrease in revenues'},
                        {'cause': 'Slower than anticipated rollout of 5G',
                         'consequence': 'Decrease in cash flow'},
                        {'cause': 'Potential disruption from ORAN',
                         'consequence': 'Decrease in revenues'},
                        {'cause': 'Decrease in revenues',
                         'consequence': 'Difficulty meeting financial '
                                        'obligations'},
                        {'cause': 'Difficulty meeting financial obligations',
                         'consequence': 'Struggle to maintain dividend'},
                        {'cause': 'Struggle to maintain dividend',
                         'consequence': 'Difficulty meeting margin targets'},
                        {'cause': 'Decrease in cash flow',
                         'consequence': 'Difficulty meeting financial '
                                        'obligations'},
                        {'cause': 'Net cash decrease',
                         'consequence': 'Difficulty meeting financial '
                                        'obligations'},
                        {'cause': 'Restructuring charges',
                         'consequence': 'Strain on financial position'},
                        {'cause': 'Difficulty meeting margin targets',
                         'consequence': 'Potential default'}],
 'company_name': 'Telefonaktiebolaget LM Ericsson (ERICB).'}

market_comment = "Intrum is a Sweden-based credit management services company with subsidiaries in Europe. They offer services covering the entire credit management chain, ranging from prospecting and segmenting to payment management services, collection and payment services [sales ledger, reminder, e-commerce, VAT refunds, debt collection, debt surveillance]. Intrum CDS is a constituent of XOver since s28, currently rated B2/BB/BB-. Last summer, Intrum reported available liquidity SEK 13b and SEK 61.3b total debt [bonds CPs PPs RCF] and was planning to cut leverage by scrapping 2024 dividend and exiting 3 additional markets completely [Czech Republic, Slovakia and Hungary]. This week, not only did we learn that the Eastern Europe exit was not guaranteed any longer either, but Intrum also agreed to sell part of its portfolio, to raise SEK8.2b [by H1 end]. This confirms rumours from December, and covers slightly more than a third of debt due by 2025 end. So, Intrum sold two thirds what could be their crown jewel assets. The assets they could sell, anyway. For 98% of Sept 30th book value. Retaining a 35% stake in the entity acquiring them. Cerberus owning the other 65%. And Intrum agreed to keep servicing the portfolio for at least 5 years. Even before the call Tuesday morning, this benefitted the very short end of the curve, but was already slight negative for the longer end and for the equity [FCF dilutive, increasing leverage]. While Tuesday morning started around 13.5% upfront [5-year / before the call], Intrum CDS had reached 18% upfront by noon, and widened a total +8pt over two sessions. Skepticism about achieving servicing targets, reduced RCF capacity, limited asset sales prospects going forward, are likely to keep Intrum CDS in the top 3 of widest names in crossover benchmark. Its now reverted tighter -2.5pt over the past two sessions, in a slight reversal. A successful RCF maturity extension is what could take it back further."

pprint(market_comment)

('Intrum is a Sweden-based credit management services company with '
 'subsidiaries in Europe. They offer services covering the entire credit '
 'management chain, ranging from prospecting and segmenting to payment '
 'management services, collection and payment services [sales ledger, '
 'reminder, e-commerce, VAT refunds, debt collection, debt surveillance]. '
 'Intrum CDS is a constituent of XOver since s28, currently rated B2/BB/BB-. '
 'Last summer, Intrum reported available liquidity SEK 13b and SEK 61.3b total '
 'debt [bonds CPs PPs RCF] and was planning to cut leverage by scrapping 2024 '
 'dividend and exiting 3 additional markets completely [Czech Republic, '
 'Slovakia and Hungary]. This week, not only did we learn that the Eastern '
 'Europe exit was not guaranteed any longer either, but Intrum also agreed to '
 'sell part of its portfolio, to raise SEK8.2b [by H1 end]. This confirms '
 'rumours from December, and covers slightly more than a third of debt due by '
 '2025 end. So, Intrum sold two thirds what could be their crown jewel assets. '
 'The assets they could sell, anyway. For 98% of Sept 30th book value. '
 'Retaining a 35% stake in the entity acquiring them. Cerberus owning the '
 'other 65%. And Intrum agreed to keep servicing the portfolio for at least 5 '
 'years. Even before the call Tuesday morning, this benefitted the very short '
 'end of the curve, but was already slight negative for the longer end and for '
 'the equity [FCF dilutive, increasing leverage]. While Tuesday morning '
 'started around 13.5% upfront [5-year / before the call], Intrum CDS had '
 'reached 18% upfront by noon, and widened a total +8pt over two sessions. '
 'Skepticism about achieving servicing targets, reduced RCF capacity, limited '
 'asset sales prospects going forward, are likely to keep Intrum CDS in the '
 'top 3 of widest names in crossover benchmark. Its now reverted tighter '
 '-2.5pt over the past two sessions, in a slight reversal. A successful RCF '
 'maturity extension is what could take it back further.')

result = await causal_mechanism_bankruptcy(market_comment)

pprint(result)

{'causal_explanation': [{'cause': 'Insufficient liquidity',
                         'consequence': 'Difficulty meeting debt obligations'},
                        {'cause': 'High debt levels',
                         'consequence': 'Difficulty meeting debt obligations'},
                        {'cause': 'Inability to cut leverage',
                         'consequence': 'Difficulty meeting debt obligations'},
                        {'cause': 'Inability to exit markets',
                         'consequence': 'Difficulty meeting debt obligations'},
                        {'cause': 'Sale of portfolio at discounted price',
                         'consequence': 'Difficulty meeting debt obligations'},
                        {'cause': 'Retaining stake in acquiring entity',
                         'consequence': 'Difficulty meeting debt obligations'},
                        {'cause': 'Inability to meet servicing targets',
                         'consequence': 'Difficulty meeting debt obligations'},
                        {'cause': 'Reduced RCF capacity',
                         'consequence': 'Difficulty meeting debt obligations'},
                        {'cause': 'Limited asset sales prospects',
                         'consequence': 'Difficulty meeting debt obligations'},
                        {'cause': 'Widening of CDS',
                         'consequence': 'Difficulty meeting debt obligations'},
                        {'cause': 'Failure to extend RCF maturity',
                         'consequence': 'Difficulty meeting debt obligations'},
                        {'cause': 'Inability to improve financial position',
                         'consequence': 'Difficulty meeting debt obligations'},
                        {'cause': 'Difficulty meeting debt obligations',
                         'consequence': 'DEFAULT'}],
 'company_name': 'Intrum'}

market_comment = "CMA CGM SA [CMACG] is the #3 global shipping co. The services they provide include vessel/container fleet management, freight delivery, logistics, cargo cruises. CMA CGM is privately controlled by the Saade family [founders]. CMA CGM is HQ’d in Marseille and sponsors the [un]beloved OM football club. CMA CGM CDS has been in CrossOver indices from s25 to s38. After reaching 50% upfront at the peak of the covid financial crisis, CMA CGM CDS tightened massively and continuously, with conservative balance-sheet management coupled with surging earnings in the subsequent 2 years, thanks to high freight rates and saturated supply chains. While CMA CGM had less than €1.7B cash at EoY-20, they reported €11.3B cash at EoY-22. Jan 30 last year, CMA-CGM 5-year CDS spread squeezed tighter -110bp to 285bp, after reports that they’d redeem €525M 7.5% 26s at 103.75. Some analysts at the time noted CMACG CDS might find itself without any deliverable obligation, temporarily orphan. Since then, CMA CGM has not come back to the market, but still guarantees c€100m Fenix Marine debt, c€80m first lien secured loans at SCI Tour d’Arenc, on top of the €200m+ first lien secured loans at CMA CGM SA. Last May, CMA CGM Group granted a put option to the Bolloré Group to sell the transport and logistics operations of Bolloré Logistics. CMA CGM then started reporting significantly lower net income [$2B Q1-23 from $7B+ Q1-22] marking the end of two golden years. And last July, the Bolloré Group announced that it signed the share purchase agreement to sell 100% of Bolloré Logistics to the CMA CGM Group, purchase price €4.65B ex net debt, completion subject to antitrust and foreign investment clearances. In much smaller acquisitions, the Saade family has taken a 10% stake in French broadcaster M6, a stake in online media Brut and bought newspaper La Tribune, regional newspaper La Provence. June last year, Moody’s took CMA CGM's long-term corporate family rating up one notch to Ba1, with a stable outlook [from positive], praising continued high FCF generation used to fund both logistics and terminals acquisitions, as well as debt reduction, strengthening B/S. During H2-23 and to date, the open interest reported at clearing houses on CMA CGM CDS, has been about halved, now just shy of €400m across Ice Clear and LCH, and across all maturities, highlighting the depth of the short cutting enterprise that’s been taking place, although the fact that this name is not in CrossOver indices since series s38 surely played a role. Aside October-last-year macro weakness, there has been NO exit door for shorts on CMA CGM CDS. More recently, the fact that several shipping companies announced a pause on Red Sea passages [following USUK/Houthi local war -- inducing a c30% significant extension via South Africa] is likely to boost container companies’ earnings prospects. On that, Reuters has reported that CMA CGM’s Asia-Med freight rates had soared by up to 100% [!] as of this past Monday, compared to Monday Jan 1st. Absent a short resolution in the Red Sea, 200bp does not feel like a solid floor."

pprint(market_comment)

('CMA CGM SA [CMACG] is the #3 global shipping co. The services they provide '
 'include vessel/container fleet management, freight delivery, logistics, '
 'cargo cruises. CMA CGM is privately controlled by the Saade family '
 '[founders]. CMA CGM is HQ’d in Marseille and sponsors the [un]beloved OM '
 'football club. CMA CGM CDS has been in CrossOver indices from s25 to s38. '
 'After reaching 50% upfront at the peak of the covid financial crisis, CMA '
 'CGM CDS tightened massively and continuously, with conservative '
 'balance-sheet management coupled with surging earnings in the subsequent 2 '
 'years, thanks to high freight rates and saturated supply chains. While CMA '
 'CGM had less than €1.7B cash at EoY-20, they reported €11.3B cash at EoY-22. '
 'Jan 30 last year, CMA-CGM 5-year CDS spread squeezed tighter -110bp to '
 '285bp, after reports that they’d redeem €525M 7.5% 26s at 103.75. Some '
 'analysts at the time noted CMACG CDS might find itself without any '
 'deliverable obligation, temporarily orphan. Since then, CMA CGM has not come '
 'back to the market, but still guarantees c€100m Fenix Marine debt, c€80m '
 'first lien secured loans at SCI Tour d’Arenc, on top of the €200m+ first '
 'lien secured loans at CMA CGM SA. Last May, CMA CGM Group granted a put '
 'option to the Bolloré Group to sell the transport and logistics operations '
 'of Bolloré Logistics. CMA CGM then started reporting significantly lower net '
 'income [$2B Q1-23 from $7B+ Q1-22] marking the end of two golden years. And '
 'last July, the Bolloré Group announced that it signed the share purchase '
 'agreement to sell 100% of Bolloré Logistics to the CMA CGM Group, purchase '
 'price €4.65B ex net debt, completion subject to antitrust and foreign '
 'investment clearances. In much smaller acquisitions, the Saade family has '
 'taken a 10% stake in French broadcaster M6, a stake in online media Brut and '
 'bought newspaper La Tribune, regional newspaper La Provence. June last year, '
 "Moody’s took CMA CGM's long-term corporate family rating up one notch to "
 'Ba1, with a stable outlook [from positive], praising continued high FCF '
 'generation used to fund both logistics and terminals acquisitions, as well '
 'as debt reduction, strengthening B/S. During H2-23 and to date, the open '
 'interest reported at clearing houses on CMA CGM CDS, has been about halved, '
 'now just shy of €400m across Ice Clear and LCH, and across all maturities, '
 'highlighting the depth of the short cutting enterprise that’s been taking '
 'place, although the fact that this name is not in CrossOver indices since '
 'series s38 surely played a role. Aside October-last-year macro weakness, '
 'there has been NO exit door for shorts on CMA CGM CDS. More recently, the '
 'fact that several shipping companies announced a pause on Red Sea passages '
 '[following USUK/Houthi local war -- inducing a c30% significant extension '
 'via South Africa] is likely to boost container companies’ earnings '
 'prospects. On that, Reuters has reported that CMA CGM’s Asia-Med freight '
 'rates had soared by up to 100% [!] as of this past Monday, compared to '
 'Monday Jan 1st. Absent a short resolution in the Red Sea, 200bp does not '
 'feel like a solid floor.')

result = await causal_mechanism_bankruptcy(market_comment)

pprint(result)

{'causal_explanation': [{'cause': 'High debt',
                         'consequence': 'Insufficient cash flow'},
                        {'cause': 'Reliance on unsustainable earnings',
                         'consequence': 'Decrease in earnings'},
                        {'cause': 'Recent investments and acquisitions',
                         'consequence': 'Strain on balance sheet'},
                        {'cause': 'Pause on Red Sea passages',
                         'consequence': 'Decrease in earnings'},
                        {'cause': 'Low open interest in CDS contracts',
                         'consequence': 'Decrease in creditworthiness'},
                        {'cause': 'Decrease in creditworthiness',
                         'consequence': 'Increase in likelihood of default'},
                        {'cause': 'Insufficient cash flow and strain on '
                                  'balance sheet',
                         'consequence': 'DEFAULT'}],
 'company_name': 'CMA CGM SA.'}

market_comment = "Founded in 1909 as a small family-run clinical analysis laboratory in Barcelona, Grifols S.A. is now a global pharma/chemical manufacturer, European leader as producer of blood plasma-based products. Grifols also supply devices, instruments, and reagents for clinical testing laboratories. Rated Caa1/B-/B+ Grifols has its CDS in all CrossOver indices since vintage s28 included. COVID had been the primary driver of reduced plasma volumes, and started a new era for Grifols CDS. EBITDA margin remained strong 25% [20] 20% [21] but COVID net impact on EBITDA was -€500m [lower plasma volumes -€420m] and net leverage increased in 2021 off strategic acquisitions [€520m]. Now valued €5.5B in the stock market, Grifols had reported €10B long term debt as of 2022 end. They have two outstanding bonds totaling €2B due 2025, the next major bonds maturity year being 2027 [roughly another €2B]. In June 2022, Grifols outperformed after a weak H1 [300bp -> 600bp] and Grifols stocks fell, as they were reported to be in talks to raise €2B equity. This was seen as showing a shift in Grifols family vision about leverage and control. However this was after leverage reduction had been guided in February 2022. At the time net debt €5.8B [end 2021] net leverage 5.4x, liquidity €1.3B cash €0.7B. Last June 14, Grifols 5-year CDS tightened -70bp to 430bp, as Grifols may sell off assets including the 26% in Shanghai RAAS it bought in 2019. Grifols had entered a strategic alliance with the Chinese group in 2020. Grifols SA said that they would expect to receive $1.5B if a deal goes through, boosting their debt cutting effort. The beautiful rally ended THIS WEEK. On Tuesday [Jan 9] Gotham City Research LLC stated that Grifols manipulates reported debt & EBITDA to artificially reduce reported leverage to 6x, which they believe is closer to 12x. Gotham argued that both Grifols and Scranton Enterprises [a Grifols family vehicle] fully consolidate BPC + Haema onto their financial statements, a deceptive and incorrect treatment. Gotham said that should their estimate of Grifols’ true leverage be correct, the company should face notably higher financing costs, and they believe that Grifols equities are uninvestable, “likely zero”. Gotham said Grifols fully consolidates both Haema and BPC Plasma, despite owning 0% of each company, Haema + BPC are material to Grifols as they account for c40% of Grifols earnings from non-controlling interests, Scranton Enterprises also fully consolidates BPC Plasma and Haema, these entities’ earnings appear to account for >100% of Scranton’s earnings, while Scranton owns 8.4% of Grifols. At a lesser level, Gotham claimed that Grifols lent Scranton $95m in 2018, a loan seeming tied to the BPC/Haema transaction, yet this loan was undisclosed in Grifols’ filings, only appearing in Scranton filings. Scranton’s 2021 filing shows an increase in liabilities of €59m relating to “advance payments from Grifols Worldwide Operations Ltd”, another transaction undisclosed in Grifols filings. Finally, Gotham argued that while Grifols’ CEO is hailed as a change in direction, he has actually been with Grifols since 2006, and was on the board at the time the suspect transactions occurred. Grifols put out a communique commenting upon “recent false information and speculations published by Gotham City Research regarding their accounting and financial disclosure”. In this, Grifols did deny and reject allegations of wrongful accounting or reporting practices of their consolidated financial statements, noting that their annual audits are conducted by one of the big-four audit firms. On Wednesday [Jan 10], the owner of Gotham City Research, General Industrial Partners, reduced its short position in Grifols to 0.06% from 0.6%. Job done. Grifols shares pared their two-day decline to 18%. From 43% early Tuesday. Today [Jan 12], Grifols 5-year CDS widened back roughly 90bp, on concerns about the collapse of a $1.8B deal to sell a stake in Shanghai RAAS Blood Products to a Chinese player named Haier. The market fears a breakdown in the deal due to the turmoil ignited this week by Gotham. However, some other analysts express confidence that the deal w ill be closed in the coming months."

pprint(market_comment)

('Founded in 1909 as a small family-run clinical analysis laboratory in '
 'Barcelona, Grifols S.A. is now a global pharma/chemical manufacturer, '
 'European leader as producer of blood plasma-based products. Grifols also '
 'supply devices, instruments, and reagents for clinical testing laboratories. '
 'Rated Caa1/B-/B+ Grifols has its CDS in all CrossOver indices since vintage '
 's28 included. COVID had been the primary driver of reduced plasma volumes, '
 'and started a new era for Grifols CDS. EBITDA margin remained strong 25% '
 '[20] 20% [21] but COVID net impact on EBITDA was -€500m [lower plasma '
 'volumes -€420m] and net leverage increased in 2021 off strategic '
 'acquisitions [€520m]. Now valued €5.5B in the stock market, Grifols had '
 'reported €10B long term debt as of 2022 end. They have two outstanding bonds '
 'totaling €2B due 2025, the next major bonds maturity year being 2027 '
 '[roughly another €2B]. In June 2022, Grifols outperformed after a weak H1 '
 '[300bp -> 600bp] and Grifols stocks fell, as they were reported to be in '
 'talks to raise €2B equity. This was seen as showing a shift in Grifols '
 'family vision about leverage and control. However this was after leverage '
 'reduction had been guided in February 2022. At the time net debt €5.8B [end '
 '2021] net leverage 5.4x, liquidity €1.3B cash €0.7B. Last June 14, Grifols '
 '5-year CDS tightened -70bp to 430bp, as Grifols may sell off assets '
 'including the 26% in Shanghai RAAS it bought in 2019. Grifols had entered a '
 'strategic alliance with the Chinese group in 2020. Grifols SA said that they '
 'would expect to receive $1.5B if a deal goes through, boosting their debt '
 'cutting effort. The beautiful rally ended THIS WEEK. On Tuesday [Jan 9] '
 'Gotham City Research LLC stated that Grifols manipulates reported debt & '
 'EBITDA to artificially reduce reported leverage to 6x, which they believe is '
 'closer to 12x. Gotham argued that both Grifols and Scranton Enterprises [a '
 'Grifols family vehicle] fully consolidate BPC + Haema onto their financial '
 'statements, a deceptive and incorrect treatment. Gotham said that should '
 'their estimate of Grifols’ true leverage be correct, the company should face '
 'notably higher financing costs, and they believe that Grifols equities are '
 'uninvestable, “likely zero”. Gotham said Grifols fully consolidates both '
 'Haema and BPC Plasma, despite owning 0% of each company, Haema + BPC are '
 'material to Grifols as they account for c40% of Grifols earnings from '
 'non-controlling interests, Scranton Enterprises also fully consolidates BPC '
 'Plasma and Haema, these entities’ earnings appear to account for >100% of '
 'Scranton’s earnings, while Scranton owns 8.4% of Grifols. At a lesser level, '
 'Gotham claimed that Grifols lent Scranton $95m in 2018, a loan seeming tied '
 'to the BPC/Haema transaction, yet this loan was undisclosed in Grifols’ '
 'filings, only appearing in Scranton filings. Scranton’s 2021 filing shows an '
 'increase in liabilities of €59m relating to “advance payments from Grifols '
 'Worldwide Operations Ltd”, another transaction undisclosed in Grifols '
 'filings. Finally, Gotham argued that while Grifols’ CEO is hailed as a '
 'change in direction, he has actually been with Grifols since 2006, and was '
 'on the board at the time the suspect transactions occurred. Grifols put out '
 'a communique commenting upon “recent false information and speculations '
 'published by Gotham City Research regarding their accounting and financial '
 'disclosure”. In this, Grifols did deny and reject allegations of wrongful '
 'accounting or reporting practices of their consolidated financial '
 'statements, noting that their annual audits are conducted by one of the '
 'big-four audit firms. On Wednesday [Jan 10], the owner of Gotham City '
 'Research, General Industrial Partners, reduced its short position in Grifols '
 'to 0.06% from 0.6%. Job done. Grifols shares pared their two-day decline to '
 '18%. From 43% early Tuesday. Today [Jan 12], Grifols 5-year CDS widened back '
 'roughly 90bp, on concerns about the collapse of a $1.8B deal to sell a stake '
 'in Shanghai RAAS Blood Products to a Chinese player named Haier. The market '
 'fears a breakdown in the deal due to the turmoil ignited this week by '
 'Gotham. However, some other analysts express confidence that the deal w ill '
 'be closed in the coming months.')

result = await causal_mechanism_bankruptcy(market_comment)

pprint(result)

{'causal_explanation': [{'cause': 'Decrease in plasma volumes',
                         'consequence': 'Negative impact on EBITDA'},
                        {'cause': 'Negative impact on EBITDA',
                         'consequence': 'Increase in net leverage'},
                        {'cause': 'Increase in net leverage',
                         'consequence': 'Decrease in liquidity'},
                        {'cause': 'Decrease in liquidity',
                         'consequence': 'Difficulty in meeting debt '
                                        'obligations'},
                        {'cause': 'Difficulty in meeting debt obligations',
                         'consequence': 'Potential default on debt'},
                        {'cause': 'Allegations of deceptive accounting '
                                  'practices',
                         'consequence': 'Market turmoil'},
                        {'cause': 'Market turmoil',
                         'consequence': 'Decline in stock price'},
                        {'cause': 'Decline in stock price',
                         'consequence': 'Increase in CDS'},
                        {'cause': 'Failure of $1.8B deal',
                         'consequence': 'Difficulty in managing debt'},
                        {'cause': 'Difficulty in managing debt',
                         'consequence': 'Potential default on debt'},
                        {'cause': 'Inability to secure new financing',
                         'consequence': 'Potential default on debt'},
                        {'cause': 'Potential default on debt',
                         'consequence': 'DEFAULT'}],
 'company_name': 'Grifols S.A.'}

Conclusion: In this blog post, we explored the application of LMQL, a SQL-like language for interacting with large language models (LLMs), focusing on its practical use in analyzing market comments. The examples provided demonstrate how LMQL can be employed to extract and structure specific information from textual data: LMQL enhances one’s ability to manage the output format of language models, which can occasionally be overly ‘creative’. This facilitates the integration of LLM outputs into conventional information systems that require structured inputs.

marti.ai tech blog

EMNLP 2025 in Suzhou

EMNLP 2025 in Suzhou

Motivation

Relevant (to us) EMNLP 2025 Papers

Zero-Shot Extraction of Stock Relationship Graphs with LLMs (Zhou et al., FinNLP 2025)

Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey (Chih-Kai Yang et al.)

The Medium Is Not the Message: Deconfounding Document Embeddings via Linear Concept Erasure (Fan et al.)

CafGa: Customizing Feature Attributions to Explain Language Models (Boyle et al.)

LLMs Don’t Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations (Mayne et al.)

Conclusion

Serious, Sassy, or Sad? Teaching Machines to Read the Room (From Speech Embeddings)

Serious, Sassy, or Sad? Teaching Machines to Read the Room (From Speech Embeddings)

Table of Contents

Introduction

The experiment

Research question

tl;dr

Loading the Embeddings

Understanding the Embeddings

Text Embeddings

Audio Embeddings

Residual Embeddings

Implications

Validating the Findings with Supervised Classification

Short vs. Long Sentences

The short sentences case:

The long sentences case:

🔍 Key Takeaways

Discrepancy Index – A Future Research Direction

💡 The idea:

🔹 Potential applications:

🔬 Next steps for exploration:

Disentangling Speech Embeddings: Removing Text Content from Audio Embeddings with Regression

Disentangling Speech Embeddings: Removing Text Content from Audio Embeddings with Regression

Table of Contents

1. Introduction

Background Concepts

2. Data Preparation

2.1 Loading Audio and Text Embeddings

2.2 Experimental Setup

2.3 Building DataFrames

3. Residualizing Audio Embeddings via Regression

3.1 Setting Up the Regression Problem

3.2 Fitting a Ridge Regression Model

3.3 Computing Residuals

4. Analyzing Embedding Similarities

4.1 Pairwise Similarity with Cosine Similarity for Each Voice

4.2 Same-Voice Similarity: Audio vs. Residual

4.3 Different-Voice Similarity: Audio vs. Residual

4.4 Average Similarity Across Voices

5. Discussion and Conclusion

Personal Reflections on NeurIPS 2024

Personal Reflections on NeurIPS 2024

Introduction

Key Highlights from the Conference

Personal Takeaways

What’s next?

Wrapping up

What’s in a Name? AI Meets the Sociology of Naming

What’s in a Name? AI Meets the Sociology of Naming

Introduction: Context of this blog

Introduction: Brief history of socio-onomastics

Key takeaways from socio-onomastics research

Case Study: Gautier

Historical and Cultural Context

Social and Regional Patterns

Regional Relevance

Abstract Pattern

Modern Tools and AI in Onomastics

Conclusion: Names as windows into society

[paper] Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

[paper] Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

Summary of the paper

Q&A with ChatGPT against the paper

Related work

Agent Output:

Bayesian Networks for Business: Modeling Profit and Loss of a Cafe in Hong Kong

Bayesian Networks for Business: Modeling Profit and Loss of a Cafe in Hong Kong

Network Structure: