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Megh Thakkar

Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Sarath Chandar
Sujets de recherche
Traitement du langage naturel

Publications

The BrowserGym Ecosystem for Web Agent Research
Thibault Le Sellier De Chezelles
Maxime Gasse
Alexandre Lacoste
Alexandre Drouin
Massimo Caccia
Léo Boisvert
Megh Thakkar
Tom Marty
Rim Assouel
Sahar Omidi Shayegan
Lawrence Jang
Xing Han Lu
Ori Yoran
Dehan Kong
Frank F. Xu
Siva Reddy
Quentin Cappart
Graham Neubig
Ruslan Salakhutdinov
Nicolas Chapados
The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging a… (voir plus)utomation and Large Language Models (LLMs) for web interaction tasks. Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. BrowserGym aims to solve this by providing a unified, gym-like environment with well-defined observation and action spaces, facilitating standardized evaluation across diverse benchmarks. Combined with AgentLab, a complementary framework that aids in agent creation, testing, and analysis, BrowserGym offers flexibility for integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. This standardized approach seeks to reduce the time and complexity of developing web agents, supporting more reliable comparisons and facilitating in-depth analysis of agent behaviors, and could result in more adaptable, capable agents, ultimately accelerating innovation in LLM-driven automation. As a supporting evidence, we conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across all benchmarks currently available in BrowserGym. Among other findings, our results highlight a large discrepancy between OpenAI and Anthropic's latests models, with Claude-3.5-Sonnet leading the way on almost all benchmarks, except on vision-related tasks where GPT-4o is superior. Despite these advancements, our results emphasize that building robust and efficient web agents remains a significant challenge, due to the inherent complexity of real-world web environments and the limitations of current models.
2024-12-06
ArXiv (prépublication)
doi.org
arxiv.org
The BrowserGym Ecosystem for Web Agent Research
Thibault Le Sellier De Chezelles
Maxime Gasse
Alexandre Lacoste
Alexandre Drouin
Massimo Caccia
Léo Boisvert
Megh Thakkar
Tom Marty
Rim Assouel
Sahar Omidi Shayegan
Lawrence Jang
Xing Han Lu
Ori Yoran
Dehan Kong
Frank F. Xu
Siva Reddy
Quentin Cappart
Graham Neubig
Ruslan Salakhutdinov
Nicolas Chapados
The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging a… (voir plus)utomation and Large Language Models (LLMs) for web interaction tasks. Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. BrowserGym aims to solve this by providing a unified, gym-like environment with well-defined observation and action spaces, facilitating standardized evaluation across diverse benchmarks. Combined with AgentLab, a complementary framework that aids in agent creation, testing, and analysis, BrowserGym offers flexibility for integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. This standardized approach seeks to reduce the time and complexity of developing web agents, supporting more reliable comparisons and facilitating in-depth analysis of agent behaviors, and could result in more adaptable, capable agents, ultimately accelerating innovation in LLM-driven automation. As a supporting evidence, we conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across all benchmarks currently available in BrowserGym. Among other findings, our results highlight a large discrepancy between OpenAI and Anthropic's latests models, with Claude-3.5-Sonnet leading the way on almost all benchmarks, except on vision-related tasks where GPT-4o is superior. Despite these advancements, our results emphasize that building robust and efficient web agents remains a significant challenge, due to the inherent complexity of real-world web environments and the limitations of current models.
2024-12-06
ArXiv (prépublication)
arxiv.org
arxiv.org
The BrowserGym Ecosystem for Web Agent Research
Thibault Le Sellier De Chezelles
Maxime Gasse
Alexandre Lacoste
Alexandre Drouin
Massimo Caccia
Léo Boisvert
Megh Thakkar
Tom Marty
Rim Assouel
Sahar Omidi Shayegan
Lawrence Jang
Xing Han Lu
Ori Yoran
Dehan Kong
Frank F. Xu
Siva Reddy
Quentin Cappart
Graham Neubig
Ruslan Salakhutdinov
Nicolas Chapados
The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging a… (voir plus)utomation and Large Language Models (LLMs) for web interaction tasks. Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. BrowserGym aims to solve this by providing a unified, gym-like environment with well-defined observation and action spaces, facilitating standardized evaluation across diverse benchmarks. Combined with AgentLab, a complementary framework that aids in agent creation, testing, and analysis, BrowserGym offers flexibility for integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. This standardized approach seeks to reduce the time and complexity of developing web agents, supporting more reliable comparisons and facilitating in-depth analysis of agent behaviors, and could result in more adaptable, capable agents, ultimately accelerating innovation in LLM-driven automation. As a supporting evidence, we conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across all benchmarks currently available in BrowserGym. Among other findings, our results highlight a large discrepancy between OpenAI and Anthropic's latests models, with Claude-3.5-Sonnet leading the way on almost all benchmarks, except on vision-related tasks where GPT-4o is superior. Despite these advancements, our results emphasize that building robust and efficient web agents remains a significant challenge, due to the inherent complexity of real-world web environments and the limitations of current models.
2024-12-06
ArXiv (prépublication)
arxiv.org
arxiv.org
Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs
Megh Thakkar
Yash More
Quentin Fournier
Matthew D Riemer
Pin-Yu Chen
Amal Zouaq
Payel Das
Sarath Chandar
There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruc… (voir plus)tion-tuned counterparts. However, these expert models often experience a loss in their safety abilities in the process, making them capable of generating harmful content. As a solution, we introduce an efficient and effective merging-based alignment method called \textsc{MergeAlign} that interpolates the domain and alignment vectors, creating safer domain-specific models while preserving their utility. We apply \textsc{MergeAlign} on Llama3 variants that are experts in medicine and finance, obtaining substantial alignment improvements with minimal to no degradation on domain-specific benchmarks. We study the impact of model merging through model similarity metrics and contributions of individual models being merged. We hope our findings open new research avenues and inspire more efficient development of safe expert LLMs.
2024-11-11
ArXiv (prépublication)
doi.org
arxiv.org
Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs
Megh Thakkar
Yash More
Quentin Fournier
Matthew D Riemer
Pin-Yu Chen
Amal Zouaq
Payel Das
Sarath Chandar
There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruc… (voir plus)tion-tuned counterparts. However, these expert models often experience a loss in their safety abilities in the process, making them capable of generating harmful content. As a solution, we introduce an efficient and effective merging-based alignment method called \textsc{MergeAlign} that interpolates the domain and alignment vectors, creating safer domain-specific models while preserving their utility. We apply \textsc{MergeAlign} on Llama3 variants that are experts in medicine and finance, obtaining substantial alignment improvements with minimal to no degradation on domain-specific benchmarks. We study the impact of model merging through model similarity metrics and contributions of individual models being merged. We hope our findings open new research avenues and inspire more efficient development of safe expert LLMs.
2024-11-11
ArXiv (prépublication)
doi.org
arxiv.org
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
Nicolas Chapados
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 … (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)
openreview.net
openreview.net
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
Nicolas Chapados
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 … (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)
openreview.net
openreview.net
AgentMerge: Enhancing Generalization in Fine-Tuned LLM Agents
Megh Thakkar
Léo Boisvert
Thibault Le Sellier De Chezelles
Alexandre Piché
Maxime Gasse
Alexandre Lacoste
Massimo Caccia
2024-10-10
NeurIPS.cc/2024/Workshop/AFM (poster)
openreview.net
openreview.net
Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs
Megh Thakkar
Yash More
Quentin Fournier
Matthew D Riemer
Pin-Yu Chen
Amal Zouaq
Payel Das
Sarath Chandar
Chandar Research Lab
Mila - Québec
U. Montŕeal
AI Institute
Ibm Research
Polytechnique Montréal
There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruc… (voir plus)tion-tuned counterparts. However, these expert models often experience a loss in their safety abilities in the process, making them capable of generating harmful content. As a solution, we introduce an efficient and effective merging-based alignment method called \textsc{MergeAlign} that interpolates the domain and alignment vectors, creating safer domain-specific models while preserving their utility. We apply \textsc{MergeAlign} on Llama3 variants that are experts in medicine and finance, obtaining substantial alignment improvements with minimal to no degradation on domain-specific benchmarks. We study the impact of model merging through model similarity metrics and contributions of individual models being merged. We hope our findings open new research avenues and inspire more efficient development of safe expert LLMs.
2024-10-10
NeurIPS.cc/2024/Workshop/AFM (poster)
doi.org
openreview.net
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
Nicolas Chapados
Alexandre Lacoste
Alexandre Drouin
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)
doi.org
openreview.net
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Alexandre Drouin
Maxime Gasse
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
Quentin Cappart
David Vazquez
Nicolas Chapados
Alexandre Lacoste
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques
Megh Thakkar
Quentin Fournier
Matthew D Riemer
Pin-Yu Chen
Amal Zouaq
Payel Das
Sarath Chandar
Large language models are first pre-trained on trillions of tokens and then instruction-tuned or aligned to specific preferences. While pre-… (voir plus)training remains out of reach for most researchers due to the compute required, fine-tuning has become affordable thanks to parameter-efficient methods such as LoRA and QLoRA. Alignment is known to be sensitive to the many factors involved, including the quantity and quality of data, the alignment method, and the adapter rank. However, there has not yet been an extensive study of their effect on downstream performance. To address this gap, we conduct an in-depth investigation of the impact of popular choices for three crucial axes: (i) the alignment dataset (HH-RLHF and BeaverTails), (ii) the alignment technique (SFT and DPO), and (iii) the model (LLaMA-1, Vicuna-v1.3, Mistral-7b, and Mistral-7b-Instruct). Our extensive setup spanning over 300 experiments reveals consistent trends and unexpected findings. We observe how more informative data helps with preference alignment, cases where supervised fine-tuning outperforms preference optimization, and how aligning to a distinct preference boosts performance on downstream tasks. Through our in-depth analyses, we put forward key guidelines to help researchers perform more effective parameter-efficient LLM alignment.
2024-06-07
ArXiv (prépublication)
doi.org
arxiv.org