Antoine Bosselut

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Publications associées (26)

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PEACOK: Persona Commonsense Knowledge for Consistent and Engaging Narratives

Soyoung Oh, Antoine Bosselut, Beatriz Maria Borges Ribeiro, Deniz Bayazit, Silin Gao

Sustaining coherent and engaging narratives requires dialogue or storytelling agents to understand how the personas of speakers or listeners ground the narrative. Specifically, these agents must infer personas of their listeners to produce statements that ...

Assoc Computational Linguistics-Acl2023

Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning

Boi Faltings, Robert West, Antoine Bosselut, Debjit Paul

Large language models (LLMs) have been shown to perform better when asked to reason step-by-step before answering a question. However, it is unclear to what degree the model's final answer is faithful to the stated reasoning steps. In this paper, we perfor ...

Association for Computational Linguistics (ACL)2024

Memory-Based Model Editing at Scale

Antoine Bosselut

Even the largest neural networks make errors, and once-correct predictions can become invalid as the world changes. Model editors make local updates to the behavior of base (pre-trained) models to inject updated knowledge or correct undesirable behaviors. ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Breaking the Language Barrier: Improving Cross-Lingual Reasoning with Structured Self-Attention

Karl Aberer, Antoine Bosselut, Mohammadreza Banaei, Negar Foroutan Eghlidi

In this work, we study whether multilingual language models (MultiLMs) can transfer logical reasoning abilities to other languages when they are fine-tuned for reasoning in a different language. We evaluate the cross-lingual reasoning abilities of MultiLMs ...

Association for Computational Linguistics2023

Evaluating Morphological Compositional Generalization in Large Language Models

Antoine Bosselut, Mahammad Ismayilzada

Large language models (LLMs) have demonstrated significant progress in various natural language generation and understanding tasks. However, their linguistic generalization capabilities remain questionable, raising doubts about whether these models learn l ...

Association for Computational Linguistics2025

Discovering Language-neutral Sub-networks in Multilingual Language Models

Karl Aberer, Antoine Bosselut, Rémi Philippe Lebret, Mohammadreza Banaei, Negar Foroutan Eghlidi

Multilingual pre-trained language models transfer remarkably well on cross-lingual downstream tasks. However, the extent to which they learn language-neutral representations (i.e., shared representations that encode similar phenomena across languages), and ...

Association for Computational Linguistics2022