Alexandre Drouin

Maxime Gasse

Alexandre Lacoste

Recent advancements in large language models (LLMs) have sparked interest in developing autonomous web agents capable of performing digital … (see more)tasks through web interfaces in a human-like manner. However, even the strongest closed-source models often struggle to achieve robust results on several benchmarks, while a notable performance gap exists between them and open-source counterparts. This study investigates the potential of fine-tuning to enhance the performance of a smaller, lower-performing but cost-efficient LLM by leveraging successful traces from stronger LLMs, referred to as experts. We outline a comprehensive pipeline for data collection, filtering, and supervised fine-tuning and explore various behavior cloning parameters. Our experiments provide key insights into the challenges of fine-tuning LLMs into web agents on benchmarks like MiniWoB and WorkArena. Notably, we find that the fine-tuned agents' ability to predict expert trajectories does not consistently lead to improved downstream task performance. This raises issues such as off-policy bias and the loss of reasoning abilities during fine-tuning. We discuss potential solutions to these challenges and make both the codebase and a dataset of 140M tokens open-source for the community to build upon.

2024-10-22

NeurIPS.cc/2024/Workshop/OWA (poster)

Context is Key: A Benchmark for Forecasting with Essential Textual Information

Andrew Robert Williams

Arjun Ashok

Étienne Marcotte

Valentina Zantedeschi

Jithendaraa Subramanian

Roland Riachi

James Requeima

Alexandre Lacoste

Irina Rish

2024-10-10

NeurIPS.cc/2024/Workshop/TSALM (published)

Gintare Karolina Dziugaite

Evaluating Interventional Reasoning Capabilities of Large Language Models

Tejas Kasetty

Divyat Mahajan

Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners consid… (see more)er using large language models (LLMs) to automate decisions, studying their causal reasoning capabilities becomes crucial. A recent line of work evaluates LLMs ability to retrieve commonsense causal facts, but these evaluations do not sufficiently assess how LLMs reason about interventions. Motivated by the role that interventions play in causal inference, in this paper, we conduct empirical analyses to evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention. We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types, and enable a study of intervention-based reasoning. These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts. Our analysis on four LLMs highlights that while GPT- 4 models show promising accuracy at predicting the intervention effects, they remain sensitive to distracting factors in the prompts.

2024-10-10

NeurIPS.cc/2024/Workshop/CALM (poster)

Causal Representation Learning in Temporal Data via Single-Parent Decoding

Philippe Brouillard

Sébastien Lachapelle

Julia Kaltenborn

Yaniv Gurwicz

Peer Nowack

Jakob Runge

David Rolnick

Scientific research often seeks to understand the causal structure underlying high-level variables in a system. For example, climate scienti… (see more)sts study how phenomena, such as El Ni\~no, affect other climate processes at remote locations across the globe. However, scientists typically collect low-level measurements, such as geographically distributed temperature readings. From these, one needs to learn both a mapping to causally-relevant latent variables, such as a high-level representation of the El Ni\~no phenomenon and other processes, as well as the causal model over them. The challenge is that this task, called causal representation learning, is highly underdetermined from observational data alone, requiring other constraints during learning to resolve the indeterminacies. In this work, we consider a temporal model with a sparsity assumption, namely single-parent decoding: each observed low-level variable is only affected by a single latent variable. Such an assumption is reasonable in many scientific applications that require finding groups of low-level variables, such as extracting regions from geographically gridded measurement data in climate research or capturing brain regions from neural activity data. We demonstrate the identifiability of the resulting model and propose a differentiable method, Causal Discovery with Single-parent Decoding (CDSD), that simultaneously learns the underlying latents and a causal graph over them. We assess the validity of our theoretical results using simulated data and showcase the practical validity of our method in an application to real-world data from the climate science field.

2024-10-09

ArXiv (preprint)

Sample Compression Hypernetworks: From Generalization Bounds to Meta-Learning

Benjamin Leblanc

Mathieu Bazinet

Nathaniel D'Amours

Pascal Germain

Reconstruction functions are pivotal in sample compression theory, a framework for deriving tight generalization bounds. From a small sample… (see more) of the training set (the compression set) and an optional stream of information (the message), they recover a predictor previously learned from the whole training set. While usually fixed, we propose to learn reconstruction functions. To facilitate the optimization and increase the expressiveness of the message, we derive a new sample compression generalization bound for real-valued messages. From this theoretical analysis, we then present a new hypernetwork architecture that outputs predictors with tight generalization guarantees when trained using an original meta-learning framework. The results of promising preliminary experiments are then reported.

2024-10-09

NeurIPS.cc/2024/Workshop/Compression (published)

WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks

Léo Boisvert

Megh Thakkar

Maxime Gasse

Massimo Caccia

Thibault Le Sellier de Chezelles

Quentin Cappart

Alexandre Lacoste

The ability of large language models (LLMs) to mimic human-like intelligence has led to a surge in LLM-based autonomous agents. Though recen… (see more)t LLMs seem capable of planning and reasoning given user instructions, their effectiveness in applying these capabilities for autonomous task solving remains underexplored. This is especially true in enterprise settings, where automated agents hold the promise of a high impact. To fill this gap, we propose WorkArena++, a novel benchmark consisting of 682 tasks corresponding to realistic workflows routinely performed by knowledge workers. WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents. Our empirical studies across state-of-the-art LLMs and vision-language models (VLMs), as well as human workers, reveal several challenges for such models to serve as useful assistants in the workplace. In addition to the benchmark, we provide a mechanism to effortlessly generate thousands of ground-truth observation/action traces, which can be used for fine-tuning existing models. Overall, we expect this work to serve as a useful resource to help the community progress toward capable autonomous agents. The benchmark can be found at https://github.com/ServiceNow/WorkArena/tree/workarena-plus-plus.

2024-09-26

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)

InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation

Gaurav Sahu

Abhay Puri

Juan A. Rodriguez

Perouz Taslakian

Valentina Zantedeschi

Alexandre Lacoste

David Vazquez

Chris Pal

Sai Rajeswar

Issam Hadj Laradji

Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We intro… (see more)duce InsightBench, a benchmark dataset with three key features. First, it consists of 100 datasets representing diverse business use cases such as finance and incident management, each accompanied by a carefully curated set of insights planted in the datasets. Second, unlike existing benchmarks focusing on answering single queries, InsightBench evaluates agents based on their ability to perform end-to-end data analytics, including formulating questions, interpreting answers, and generating a summary of insights and actionable steps. Third, we conducted comprehensive quality assurance to ensure that each dataset in the benchmark had clear goals and included relevant and meaningful questions and analysis. Furthermore, we implement a two-way evaluation mechanism using LLaMA-3 as an effective, open-source evaluator to assess agents' ability to extract insights. We also propose AgentPoirot, our baseline data analysis agent capable of performing end-to-end data analytics. Our evaluation on InsightBench shows that AgentPoirot outperforms existing approaches (such as Pandas Agent) that focus on resolving single queries. We also compare the performance of open- and closed-source LLMs and various evaluation strategies. Overall, this benchmark serves as a testbed to motivate further development in comprehensive automated data analytics.

2024-07-08

ArXiv (preprint)

InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation

Gaurav Sahu

Abhay Puri

Juan A. Rodriguez

Perouz Taslakian

Valentina Zantedeschi

Alexandre Lacoste

David Vazquez

Chris Pal

Sai Rajeswar

Issam Hadj Laradji

Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We intro… (see more)duce InsightBench, a benchmark dataset with three key features. First, it consists of 100 datasets representing diverse business use cases such as finance and incident management, each accompanied by a carefully curated set of insights planted in the datasets. Second, unlike existing benchmarks focusing on answering single queries, InsightBench evaluates agents based on their ability to perform end-to-end data analytics, including formulating questions, interpreting answers, and generating a summary of insights and actionable steps. Third, we conducted comprehensive quality assurance to ensure that each dataset in the benchmark had clear goals and included relevant and meaningful questions and analysis. Furthermore, we implement a two-way evaluation mechanism using LLaMA-3 as an effective, open-source evaluator to assess agents' ability to extract insights. We also propose AgentPoirot, our baseline data analysis agent capable of performing end-to-end data analytics. Our evaluation on InsightBench shows that AgentPoirot outperforms existing approaches (such as Pandas Agent) that focus on resolving single queries. We also compare the performance of open- and closed-source LLMs and various evaluation strategies. Overall, this benchmark serves as a testbed to motivate further development in comprehensive automated data analytics.

2024-07-08

ArXiv (preprint)

InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation

Gaurav Sahu

Abhay Puri

Juan A. Rodriguez

Perouz Taslakian

Valentina Zantedeschi

Alexandre Lacoste

David Vazquez

Chris Pal

Sai Rajeswar

Issam Hadj Laradji

Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We intro… (see more)duce InsightBench, a benchmark dataset with three key features. First, it consists of 100 datasets representing diverse business use cases such as finance and incident management, each accompanied by a carefully curated set of insights planted in the datasets. Second, unlike existing benchmarks focusing on answering single queries, InsightBench evaluates agents based on their ability to perform end-to-end data analytics, including formulating questions, interpreting answers, and generating a summary of insights and actionable steps. Third, we conducted comprehensive quality assurance to ensure that each dataset in the benchmark had clear goals and included relevant and meaningful questions and analysis. Furthermore, we implement a two-way evaluation mechanism using LLaMA-3 as an effective, open-source evaluator to assess agents' ability to extract insights. We also propose AgentPoirot, our baseline data analysis agent capable of performing end-to-end data analytics. Our evaluation on InsightBench shows that AgentPoirot outperforms existing approaches (such as Pandas Agent) that focus on resolving single queries. We also compare the performance of open- and closed-source LLMs and various evaluation strategies. Overall, this benchmark serves as a testbed to motivate further development in comprehensive automated data analytics.

2024-07-08

ArXiv (preprint)

WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?

Maxime Gasse

Massimo Caccia

Issam Hadj Laradji

Manuel Del Verme

Tom Marty

Léo Boisvert

Megh Thakkar

Quentin Cappart

David Vazquez

Alexandre Lacoste

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (published)

Gintare Karolina Dziugaite

Evaluating Interventional Reasoning Capabilities of Large Language Models

Tejas Kasetty

Divyat Mahajan

Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners consid… (see more)er using large language models (LLMs) to automate decisions, studying their causal reasoning capabilities becomes crucial. A recent line of work evaluates LLMs ability to retrieve commonsense causal facts, but these evaluations do not sufficiently assess how LLMs reason about interventions. Motivated by the role that interventions play in causal inference, in this paper, we conduct empirical analyses to evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention. We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types, and enable a study of intervention-based reasoning. These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts. We evaluate six LLMs on the benchmarks, finding that GPT models show promising accuracy at predicting the intervention effects.

2024-04-08

ArXiv (preprint)

Gintare Karolina Dziugaite

Evaluating Interventional Reasoning Capabilities of Large Language Models

Tejas Kasetty

Divyat Mahajan

Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners consid… (see more)er using large language models (LLMs) to automate decisions, studying their causal reasoning capabilities becomes crucial. A recent line of work evaluates LLMs ability to retrieve commonsense causal facts, but these evaluations do not sufficiently assess how LLMs reason about interventions. Motivated by the role that interventions play in causal inference, in this paper, we conduct empirical analyses to evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention. We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types, and enable a study of intervention-based reasoning. These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts. We evaluate six LLMs on the benchmarks, finding that GPT models show promising accuracy at predicting the intervention effects.

2024-04-08

ArXiv (preprint)