Marylou Fauchard

Master's Research - Université de Montréal

Supervisor

Margarida Carvalho

Co-supervisor

Golnoosh Farnadi

Research Topics

AI Alignment

Large Language Models (LLM)

Operations Research

Reinforcement Learning

Publications

Objective Misalignment in LLM-based Multi Agent Social Deception Game

Large language model–based multi-agent systems have attracted increasing attention for their strong performance in collaborative tasks and… (see more) social simulations. However, these interactive settings also introduce vulnerabilities, as a single agent's hidden goals and misaligned behavior can propagate misleading or malicious information throughout the system. In this work, we study these risks in the context of social deception games. We focus on the Werewolf Game, which requires agents to reason, communicate, and collaborate under asymmetric and incomplete information. We modify the individual objectives of some agents to induce benevolent, individualistic, and malevolent strategies that can make agents depart from the objectives of their own team. We evaluate how objective divergence affects game outcomes, collaboration, and goal satisfaction. Misaligned agents often succeed in achieving their own objectives, with effects amplified by role-based power asymmetries. Qualitative analyses further show that agents remain coherent and adaptive, strategically adjusting their reasoning, communication, voting behavior, and influence on group dynamics. These results indicate that risks in LLM-based multi-agent systems extend beyond collaborative task settings and persist even in environments where competition is structurally expected.

2026-02-28

AIWILD @ International Conference on Learning Representations (published)

openreview.net

Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs

Alireza Dehghanpour Farashah

As multilingual large language models become more widely used, ensuring their safety and fairness across diverse linguistic contexts present… (see more)s unique challenges. While existing research on machine unlearning has primarily focused on monolingual settings, typically English, multilingual environments introduce additional complexities due to cross-lingual knowledge transfer and biases embedded in both pretraining and fine-tuning data. In this work, we study multilingual unlearning using the Aya-Expanse 8B model under two settings: (1) data unlearning and (2) concept unlearning. We extend benchmarks for factual knowledge and stereotypes to ten languages through translation: English, French, Arabic, Japanese, Russian, Farsi, Korean, Hindi, Hebrew, and Indonesian. These languages span five language families and a wide range of resource levels. Our experiments show that unlearning in high-resource languages is generally more stable, with asymmetric transfer effects observed between typologically related languages. Furthermore, our analysis of linguistic distances indicates that syntactic similarity is the strongest predictor of cross-lingual unlearning behavior.

2026-01-08

arXiv (preprint)

doi.org

arxiv.org

Reasoning with Preference Constraints: A Benchmark for Language Models in Many-to-One Matching Markets

2025-09-21

WiML @ Neural Information Processing Systems (published)

doi.org