Maxime Gasse

Thibault Le Sellier De Chezelles

Maîtrise recherche - Polytechnique

Co-superviseur⋅e :

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Brice Rauby

Doctorat - Polytechnique

brice.rauby@polymlt.ca

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Publications

Fine-Tuning Web Agents: It Works, But It's Trickier Than You Think

Massimo Caccia

Megh Thakkar

Léo Boisvert

Thibault Le Sellier de Chezelles

Alexandre Piché

Alexandre Lacoste

Recent advancements in large language models (LLMs) have sparked interest in developing autonomous web agents capable of performing digital … (voir plus)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)

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

Léo Boisvert

Megh Thakkar

Massimo Caccia

Thibault Le Sellier de Chezelles

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… (voir plus)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)

Deep Learning in Ultrasound Localization Microscopy: Applications and Perspectives.

Brice Rauby

Paul Xing

Jean Provost

Ultrasound Localization Microscopy (ULM) is a novel super-resolution imaging technique that can image the vasculature in vivo at depth with … (voir plus)resolution far beyond the conventional limit of diffraction. By relying on the localization and tracking of clinically approved microbubbles injected in the blood stream, ULM can provide not only anatomical visualization but also hemodynamic quantification of the microvasculature of different tissues. Various deep-learning approaches have been proposed to address challenges in ULM including denoising, improving microbubble localization, estimating blood flow velocity or performing aberration correction. Proposed deep learning methods often outperform their conventional counterparts by improving image quality and reducing processing time. In addition, their robustness to high concentrations of microbubbles can lead to reduced acquisition times in ULM, addressing a major hindrance to ULM clinical application. Herein, we propose a comprehensive review of the diversity of deep learning applications in ULM focusing on approaches assuming a sparse microbubbles distribution. We first provide an overview of how existing studies vary in the constitution of their datasets or in the tasks targeted by deep learning model. We also take a deeper look into the numerous approaches that have been proposed to improve the localization of microbubbles since they differ highly in their formulation of the optimization problem, their evaluation, or their network architectures. We finally discuss the current limitations and challenges of these methods, as well as the promises and potential of deep learning for ULM in the future.

2024-09-17

IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control (publié)

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

Massimo Caccia

Issam Hadj Laradji

Manuel Del Verme

Tom Marty

Léo Boisvert

Megh Thakkar

David Vazquez

Alexandre Lacoste

2024-07-08

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

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

Massimo Caccia

Issam Hadj Laradji

Manuel Del Verme

Tom Marty

Léo Boisvert

Megh Thakkar

David Vazquez

Alexandre Lacoste

We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuri… (voir plus)ng the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterprise software systems. To this end, we propose WorkArena, a remote-hosted benchmark of 29 tasks based on the widely-used ServiceNow platform. We also introduce BrowserGym, an environment for the design and evaluation of such agents, offering a rich set of actions as well as multimodal observations. Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation. Notably, our analysis uncovers a significant performance disparity between open and closed-source LLMs, highlighting a critical area for future exploration and development in the field.

2024-03-12

ArXiv (prépublication)

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

Massimo Caccia

Issam Hadj Laradji

Manuel Del Verme

Tom Marty

Léo Boisvert

Megh Thakkar

David Vazquez

Alexandre Lacoste

2024-03-11

ICLR.cc/2024/Workshop/LLMAgents (poster)

Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy

Brice Rauby

Paul Xing

Jonathan Por'ee

Jean Provost

2024-02-14

ArXiv (prépublication)

The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study

Rim Assouel

Tom Marty

Massimo Caccia

Issam Hadj Laradji

Sai Rajeswar

Hector Palacios

David Vazquez

Alexandre Lacoste

2023-11-07

NeurIPS.cc/2023/Workshop/FMDM (publié)

Using Confounded Data in Latent Model-Based Reinforcement Learning

Damien GRASSET

Guillaume Gaudron

Pierre-Yves Oudeyer

2023-01-01

Trans. Mach. Learn. Res. (publié)

Lookback for Learning to Branch

Prateek Gupta

Elias Boutros Khalil

Didier Chételat

Yoshua Bengio

Andrea Lodi

M. Pawan Kumar

2022-10-10

TMLR (accepté)

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights

Simon Bowly

Jonas Charfreitag

Laurent Charlin

Didier Chételat

Antonia Chmiela

Justin Dumouchelle

Ambros Gleixner

Aleksandr Kazachkov

Elias Boutros Khalil

Paweł Lichocki

Andrea Lodi

Miles Lubin

Chris J. Maddison

Christopher Morris

D. Papageorgiou

Augustin Parjadis

Sebastian Pokutta

Antoine Prouvost … (voir 22 de plus)

Lara Scavuzzo

Giulia Zarpellon

Linxin Yangm

Sha Lai

Akang Wang

Xiaodong Luo

Xiang Zhou

Haohan Huang

Sheng Cheng Shao

Yuanming Zhu

Dong Dong Zhang

Tao Manh Quan

Zixuan Cao

Yang Xu

Zhewei Huang

Shuchang Zhou

C. Binbin

He Minggui

Haoren Ren Hao

Zhang Zhiyu

An Zhiwu

Mao Kun

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused … (voir plus)on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants.

2021-01-01

NeurIPS (Competition and Demos) (publié)

On generalized surrogate duality in mixed-integer nonlinear programming

Benjamin Müller

Gonzalo Muñoz

Ambros Gleixner

Andrea Lodi

Felipe Serrano

2020-04-14

Integer Programming and Combinatorial Optimization (publié)