Portrait of Sarath Chandar

Sarath Chandar

Core Academic Member
sarath.chandar@mila.quebec
Canada CIFAR AI Chair
Associate Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering
Adjunct Professor, Université de Montréal, Department of Computer Science and Operations Research
Indian Institute of Technology Madras
Research Topics
AI Alignment
Deep Learning
Explainable AI (XAI)
Foundation Models
Interpretability
Large Language Models (LLM)
Lifelong Learning
Medical Machine Learning
Multi-Agent Systems
Natural Language Processing
Online Learning
Optimization
Recurrent Neural Networks
Reinforcement Learning
Representation Learning
Transfer Learning
Trustworthy AI
Website
Google Scholar

Biography

Sarath Chandar is an associate professor at Polytechnique Montreal's Department of Computer and Software Engineering, where he leads the Chandar Research Lab. He is also a Core Academic Member at Mila – Quebec Artificial Intelligence Institute and holds a Canada CIFAR AI Chair and the Canada Research Chair in Lifelong Machine Learning.

Chandar’s research interests include lifelong learning, deep learning, optimization, reinforcement learning and natural language processing. To promote research in lifelong learning, Chandar created the Conference on Lifelong Learning Agents (CoLLAs) in 2022, for which he served as program chair in 2022 and 2023.

He has a PhD from Université de Montréal and an MSc (By Research) from the Indian Institute of Technology Madras.

Current Students

Ista Abbes
Master's Research - Université de Montréal
Davide Baldelli
PhD - Polytechnique Montréal
Co-supervisor :
Amal Zouaq
Website
Github
Google Scholar
Antoine Clavaud
Master's Research - Polytechnique Montréal
Github
Naga Karthik Enamundram
PhD - Polytechnique Montréal
Principal supervisor :
Julien Cohen-Adad
emvnagakarthik@gmail.com
Website
Github
Google Scholar
Prashant Govindarajan
PhD - Polytechnique Montréal
Website
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Simon Guiroy
PhD - Université de Montréal
Principal supervisor :
Chris Pal
Website
Github
Google Scholar
Anshul Gupta
Collaborating researcher - Université de Montréal
Principal supervisor :
Liam Paull
Website
Maryam Hashemzadeh
PhD - Université de Montréal
Website
Google Scholar
David Heurtel--Depeiges
PhD - Polytechnique Montréal
Website
Github
Google Scholar
Jerry Huang
PhD - Université de Montréal
Amir Ardalan Kalantari Dehaghi
Collaborating Alumni
Github
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Lola Le Breton
Master's Research - Polytechnique Montréal
Website
Github
Ekaterina Lobacheva
Postdoctorate - Université de Montréal
Website
Github
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Pranshu Malviya
PhD - Polytechnique Montréal
Website
Github
Google Scholar
Mohamed Amine Merzouk
Postdoctorate - Polytechnique Montréal
Principal supervisor :
Foutse Khomh
Website
Github
Google Scholar
Roshan Balaji Munirathinam Sankaran Balaji
Research Intern - Polytechnique Montréal
Website
Github
Google Scholar
Hadi NekoeiQachkanloo
PhD - Université de Montréal
Darshan Patil
PhD - Université de Montréal
Website
Github
Gabriele Prato
PhD - Université de Montréal
Website
Github
Google Scholar
Mathieu Reymond
Postdoctorate
Github
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Francois Rivest
Independent visiting researcher
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Mohammad R. Samsami
Master's Research - Université de Montréal
Website
Github
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Nour Shaheen
Master's Research - Polytechnique Montréal
Github
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Megh Thakkar
Master's Research - Université de Montréal
Arjun Vaithilingam Sudhakar
PhD - Polytechnique Montréal
Kowen Woo
Research Intern - Polytechnique Montréal
Website
Github
Abdelrahman Zayed
PhD - Polytechnique Montréal
Website
Google Scholar
Xutong Zhao
PhD - Polytechnique Montréal
Artem Zholus
PhD - Polytechnique Montréal
Website
Github
Google Scholar

Publications

Torque-Aware Momentum
Pranshu Malviya
Goncalo Mordido
Aristide Baratin
Reza Babanezhad Harikandeh
Gintare Karolina Dziugaite
Razvan Pascanu
Sarath Chandar
2024-12-25
ArXiv (preprint)
doi.org
arxiv.org
Torque-Aware Momentum
Pranshu Malviya
Goncalo Mordido
Aristide Baratin
Reza Babanezhad Harikandeh
Gintare Karolina Dziugaite
Razvan Pascanu
Sarath Chandar
Efficiently exploring complex loss landscapes is key to the performance of deep neural networks. While momentum-based optimizers are widely … (see more)used in state-of-the-art setups, classical momentum can still struggle with large, misaligned gradients, leading to oscillations. To address this, we propose Torque-Aware Momentum (TAM), which introduces a damping factor based on the angle between the new gradients and previous momentum, stabilizing the update direction during training. Empirical results show that TAM, which can be combined with both SGD and Adam, enhances exploration, handles distribution shifts more effectively, and improves generalization performance across various tasks, including image classification and large language model fine-tuning, when compared to classical momentum-based optimizers.
2024-12-25
ArXiv (preprint)
doi.org
arxiv.org
Torque-Aware Momentum
Pranshu Malviya
Goncalo Mordido
Aristide Baratin
Reza Babanezhad Harikandeh
Gintare Karolina Dziugaite
Razvan Pascanu
Sarath Chandar
Efficiently exploring complex loss landscapes is key to the performance of deep neural networks. While momentum-based optimizers are widely … (see more)used in state-of-the-art setups, classical momentum can still struggle with large, misaligned gradients, leading to oscillations. To address this, we propose Torque-Aware Momentum (TAM), which introduces a damping factor based on the angle between the new gradients and previous momentum, stabilizing the update direction during training. Empirical results show that TAM, which can be combined with both SGD and Adam, enhances exploration, handles distribution shifts more effectively, and improves generalization performance across various tasks, including image classification and large language model fine-tuning, when compared to classical momentum-based optimizers.
2024-12-25
ArXiv (preprint)
doi.org
arxiv.org
Too Big to Fool: Resisting Deception in Language Models
Mohammad Reza Samsami
M. L. Richter
Juan Rodriguez
Megh Thakkar
Sarath Chandar
Maxime Gasse
Large language models must balance their weight-encoded knowledge with in-context information from prompts to generate accurate responses. T… (see more)his paper investigates this interplay by analyzing how models of varying capacities within the same family handle intentionally misleading in-context information. Our experiments demonstrate that larger models exhibit higher resilience to deceptive prompts, showcasing an advanced ability to interpret and integrate prompt information with their internal knowledge. Furthermore, we find that larger models outperform smaller ones in following legitimate instructions, indicating that their resilience is not due to disregarding in-context information. We also show that this phenomenon is likely not a result of memorization but stems from the models' ability to better leverage implicit task-relevant information from the prompt alongside their internally stored knowledge.
2024-12-13
ArXiv (preprint)
arxiv.org
arxiv.org
Too Big to Fool: Resisting Deception in Language Models
Mohammad Reza Samsami
Mats Leon Richter
Juan A. Rodriguez
Megh Thakkar
Sarath Chandar
Maxime Gasse
Large language models must balance their weight-encoded knowledge with in-context information from prompts to generate accurate responses. T… (see more)his paper investigates this interplay by analyzing how models of varying capacities within the same family handle intentionally misleading in-context information. Our experiments demonstrate that larger models exhibit higher resilience to deceptive prompts, showcasing an advanced ability to interpret and integrate prompt information with their internal knowledge. Furthermore, we find that larger models outperform smaller ones in following legitimate instructions, indicating that their resilience is not due to disregarding in-context information. We also show that this phenomenon is likely not a result of memorization but stems from the models' ability to better leverage implicit task-relevant information from the prompt alongside their internally stored knowledge.
2024-12-13
ArXiv (preprint)
doi.org
openreview.net
Too Big to Fool: Resisting Deception in Language Models
Mohammad Reza Samsami
M. L. Richter
Juan Rodriguez
Megh Thakkar
Sarath Chandar
Maxime Gasse
Large language models must balance their weight-encoded knowledge with in-context information from prompts to generate accurate responses. T… (see more)his paper investigates this interplay by analyzing how models of varying capacities within the same family handle intentionally misleading in-context information. Our experiments demonstrate that larger models exhibit higher resilience to deceptive prompts, showcasing an advanced ability to interpret and integrate prompt information with their internal knowledge. Furthermore, we find that larger models outperform smaller ones in following legitimate instructions, indicating that their resilience is not due to disregarding in-context information. We also show that this phenomenon is likely not a result of memorization but stems from the models' ability to better leverage implicit task-relevant information from the prompt alongside their internally stored knowledge.
2024-12-13
ArXiv (preprint)
doi.org
arxiv.org
Too Big to Fool: Resisting Deception in Language Models
Mohammad Reza Samsami
M. L. Richter
Juan Rodriguez
Megh Thakkar
Sarath Chandar
Maxime Gasse
Large language models must balance their weight-encoded knowledge with in-context information from prompts to generate accurate responses. T… (see more)his paper investigates this interplay by analyzing how models of varying capacities within the same family handle intentionally misleading in-context information. Our experiments demonstrate that larger models exhibit higher resilience to deceptive prompts, showcasing an advanced ability to interpret and integrate prompt information with their internal knowledge. Furthermore, we find that larger models outperform smaller ones in following legitimate instructions, indicating that their resilience is not due to disregarding in-context information. We also show that this phenomenon is likely not a result of memorization but stems from the models' ability to better leverage implicit task-relevant information from the prompt alongside their internally stored knowledge.
2024-12-13
ArXiv (preprint)
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… (see more)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 (preprint)
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… (see more)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 (preprint)
doi.org
arxiv.org
Crystal Design Amidst Noisy DFT Signals: A Reinforcement Learning Approach
Prashant Govindarajan
Mathieu Reymond
Santiago Miret
Mariano Phielipp
Sarath Chandar
2024-11-03
NeurIPS.cc/2024/Workshop/AI4Mat (published)
openreview.net
openreview.net
Do Robot Snakes Dream like Electric Sheep? Investigating the Effects of Architectural Inductive Biases on Hallucination
Jerry Huang
Prasanna Parthasarathi
Mehdi Rezagholizadeh
Boxing Chen
Sarath Chandar
The growth in prominence of large language models (LLMs) in everyday life can be largely attributed to their generative abilities, yet some … (see more)of this is also owed to the risks and costs associated with their use. On one front is their tendency to \textit{hallucinate} false or misleading information, limiting their reliability. On another is the increasing focus on the computational limitations associated with traditional self-attention based LLMs, which has brought about new alternatives, in particular recurrent models, meant to overcome them. Yet it remains uncommon to consider these two concerns simultaneously. Do changes in architecture exacerbate/alleviate existing concerns about hallucinations? Do they affect how and where they occur? Through an extensive evaluation, we study how these architecture-based inductive biases affect the propensity to hallucinate. While hallucination remains a general phenomenon not limited to specific architectures, the situations in which they occur and the ease with which specific types of hallucinations can be induced can significantly differ based on the model architecture. These findings highlight the need for better understanding both these problems in conjunction with each other, as well as consider how to design more universal techniques for handling hallucinations.
2024-10-22
ArXiv (preprint)
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… (see more)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