Portrait de Milan Bhan

Milan Bhan

Collaborateur·rice de recherche
Superviseur⋅e principal⋅e
Sarath Chandar
Sujets de recherche
Explicabilité
Grands modèles de langage (LLM)
Interprétabilité
Interprétabilité mécanistique
XAI (IA explicable)

Publications

Did I Faithfully Say What I Thought? Bridging the Gap Between Neural Activity and Self-Explanations in Large Language Models
Milan Bhan
Jean-Noël Vittaut
Nicolas Chesneau
Sarath Chandar
Marie-Jeanne Lesot
Large Language Models (LLM) have demonstrated the capability of generating free text self Natural Language Explanation (self-NLE) to justify… (voir plus) their answers. Despite their logical appearance, self-NLE do not necessarily reflect the LLM actual decision-making process, making such explanations unfaithful. While existing methods for measuring self-NLE faithfulness mostly rely on behavioral tests or computational block identification, none of them examines the neural activity underlying the model's reasoning. This work introduces a novel flexible framework for quantitatively measuring the faithfulness of LLM-generated self-NLE by directly comparing the latter with interpretations of the model's internal hidden states. The proposed framework is versatile and provides deep insights into self-NLE faithfulness by establishing a direct connection between self-NLE and model reasoning. This approach advances the understanding of self-NLE faithfulness and provides building blocks for generating more faithful self-NLE.
2025-06-10
ArXiv (prépublication)
arxiv.org
arxiv.org
Did I Faithfully Say What I Thought? Bridging the Gap Between Neural Activity and Self-Explanations in Large Language Models
Milan Bhan
Jean-Noël Vittaut
Nicolas Chesneau
Sarath Chandar
Marie-Jeanne Lesot
Large Language Models (LLM) have demonstrated the capability of generating free text self Natural Language Explanation (self-NLE) to justify… (voir plus) their answers. Despite their logical appearance, self-NLE do not necessarily reflect the LLM actual decision-making process, making such explanations unfaithful. While existing methods for measuring self-NLE faithfulness mostly rely on behavioral tests or computational block identification, none of them examines the neural activity underlying the model's reasoning. This work introduces a novel flexible framework for quantitatively measuring the faithfulness of LLM-generated self-NLE by directly comparing the latter with interpretations of the model's internal hidden states. The proposed framework is versatile and provides deep insights into self-NLE faithfulness by establishing a direct connection between self-NLE and model reasoning. This approach advances the understanding of self-NLE faithfulness and provides building blocks for generating more faithful self-NLE.
2025-06-10
ArXiv (prépublication)
doi.org
arxiv.org