Sarath Chandar

Biographie

Sarath Chandar est professeur associé au départment de génie informatique et génie logiciel de Polytechnique Montréal, où il dirige le laboratoire de recherche Chandar. Il est également membre académique principal à Mila – Institut québécois d’intelligence artificielle, et titulaire d'une chaire en IA Canada-CIFAR et d'une Chaire de recherche du Canada en apprentissage machine permanent.

Ses recherches portent sur l'apprentissage tout au long de la vie, l'apprentissage profond, l'optimisation, l'apprentissage par renforcement et le traitement du langage naturel. Pour promouvoir la recherche sur l'apprentissage tout au long de la vie, Sarath Chandar a créé la Conférence sur les agents d'apprentissage tout au long de la vie (CoLLAs) en 2022 et a présidé le programme en 2022 et en 2023. Il est titulaire d'un doctorat de l'Université de Montréal et d'une maîtrise en recherche de l'Indian Institute of Technology Madras.

Étudiants actuels

Ista Abbes

Maîtrise recherche - UdeM

Davide Baldelli

Doctorat - Polytechnique

Co-superviseur⋅e :

Maîtrise recherche - Polytechnique

Louis Clouatre

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Naga Karthik Enamundram

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Julien Cohen-Adad

emvnagakarthik@gmail.com

Prashant Govindarajan

Doctorat - Polytechnique

Simon Guiroy

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

David Heurtel--Depeiges

Doctorat - Polytechnique

Amir Ardalan Kalantari Dehaghi

Jerry Huang

Doctorat - UdeM

Collaborateur·rice alumni

andreas.madsen@mila.quebec

Lola Le Breton

Maîtrise recherche - Polytechnique

Postdoctorat - UdeM

Doctorat - Polytechnique

Co-superviseur⋅e :

Siva Reddy

Doctorat - Polytechnique

Maîtrise recherche - Polytechnique

Mohamed Amine Merzouk

Postdoctorat - Polytechnique

Superviseur⋅e principal⋅e :

Hadi NekoeiQachkanloo

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Janarthanan Rajendran

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Postdoctorat

Visiteur de recherche indépendant

Arjun Vaithilingam Sudhakar

Mohammad R. Samsami

Maîtrise recherche - UdeM

Maîtrise recherche - UdeM

Doctorat - Polytechnique

Abdelrahman Zayed

Doctorat - Polytechnique

Xutong Zhao

Doctorat - Polytechnique

Artem Zholus

Doctorat - Polytechnique

NeoBERT: une nouvelle frontière pour les modèles de langage encodeurs open-source

Billets de blogue

A digital picture of Bert from Sesame street, wering black trench coat and sunglasses

3 mars 2025

par

Lola Le Breton

Quentin Fournier

Sarath Chandar

Lire l'article

1 octobre 2024

Comment expliquer l’IA et s’assurer que cette explication est vraie? Les modèles mesurables de fidélité vous indiquent comment y parvenir

par

Andrea Madsen

Siva Reddy

Sarath Chandar

Lire l'article

Publications

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

Payel Das

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)

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

Payel Das

2024-11-11

ArXiv (prépublication)

Crystal Design Amidst Noisy DFT Signals: A Reinforcement Learning Approach

Prashant Govindarajan

Mathieu Reymond

Santiago Miret

Mariano Phielipp

2024-11-03

NeurIPS.cc/2024/Workshop/AI4Mat (publié)

Do Robot Snakes Dream like Electric Sheep? Investigating the Effects of Architectural Inductive Biases on Hallucination

Jerry Huang

Prasanna Parthasarathi

Mehdi Rezagholizadeh

Boxing Chen

The growth in prominence of large language models (LLMs) in everyday life can be largely attributed to their generative abilities, yet some … (voir plus)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 (prépublication)

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

Payel Das

2024-10-10

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

Toward Debugging Deep Reinforcement Learning Programs with RLExplorer

Rached Bouchoucha

Ahmed Haj Yahmed

Darshan Patil

Janarthanan Rajendran

Amin Nikanjam

Foutse Khomh

Deep reinforcement learning (DRL) has shown success in diverse domains such as robotics, computer games, and recommendation systems. However… (voir plus), like any other software system, DRL-based software systems are susceptible to faults that pose unique challenges for debugging and diagnosing. These faults often result in unexpected behavior without explicit failures and error messages, making debugging difficult and time-consuming. Therefore, automating the monitoring and diagnosis of DRL systems is crucial to alleviate the burden on developers. In this paper, we propose RLExplorer, the first fault diagnosis approach for DRL-based software systems. RLExplorer automatically monitors training traces and runs diagnosis routines based on properties of the DRL learning dynamics to detect the occurrence of DRL-specific faults. It then logs the results of these diagnoses as warnings that cover theoretical concepts, recommended practices, and potential solutions to the identified faults. We conducted two sets of evaluations to assess RLExplorer. Our first evaluation of faulty DRL samples from Stack Overflow revealed that our approach can effectively diagnose real faults in 83% of the cases. Our second evaluation of RLExplorer with 15 DRL experts/developers showed that (1) RLExplorer could identify 3.6 times more defects than manual debugging and (2) RLExplorer is easily integrated into DRL applications.

2024-10-06

ArXiv (prépublication)

Toward Debugging Deep Reinforcement Learning Programs with RLExplorer

Rached Bouchoucha

Ahmed Haj Yahmed

Darshan Patil

Janarthanan Rajendran

Amin Nikanjam

Foutse Khomh

2024-10-06

2024 IEEE International Conference on Software Maintenance and Evolution (ICSME) (publié)

Balancing Context Length and Mixing Times for Reinforcement Learning at Scale

Matthew D Riemer

Khimya Khetarpal

Janarthanan Rajendran

Mila Janarthanan

É. Montréal

2024-09-25

NeurIPS.cc/2024/Conference (poster)

Protein Language Models: Is Scaling Necessary?

Quentin Fournier

Robert M. Vernon

Almer van der Sloot

Benjamin Schulz

Christopher James Langmead

2024-09-23

bioRxiv (prépublication)

Protein Language Models: Is Scaling Necessary?

Quentin Fournier

Robert M. Vernon

Almer van der Sloot

Benjamin Schulz

Christopher James Langmead

Public protein sequence databases contain samples from the fitness landscape explored by nature. Protein language models (pLMs) pre-trained … (voir plus)on these sequences aim to capture this landscape for tasks like property prediction and protein design. Following the same trend as in natural language processing, pLMs have continuously been scaled up. However, the premise that scale leads to better performance assumes that source databases provide accurate representation of the underlying fitness landscape, which is likely false. By developing an efficient codebase, designing a modern architecture, and addressing data quality concerns such as sample bias, we introduce AMPLIFY, a best-in-class pLM that is orders of magnitude less expensive to train and deploy than previous models. Furthermore, to support the scientific community and democratize the training of pLMs, we have open-sourced AMPLIFY’s pre-training codebase, data, and model checkpoints.

2024-09-23

bioRxiv (prépublication)

Are self-explanations from Large Language Models faithful?

Andreas Madsen

Siva Reddy

2024-08-01

Findings of the Association for Computational Linguistics ACL 2024 (publié)

Should We Attend More or Less? Modulating Attention for Fairness

Abdelrahman Zayed

Goncalo Mordido

Samira Shabanian

2024-07-10

colmweb.org/COLM/2024/Conference (accepté)