Portrait of Milan Bhan

Milan Bhan

Collaborating researcher
Supervisor
Sarath Chandar
Research Topics
Explainability
Explainable AI (XAI)
Interpretability
Large Language Models (LLM)
Mechanistic Interpretability

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
A. Chandar
Marie-Jeanne Lesot
Large Language Models (LLM) have demonstrated the capability of generating free text self Natural Language Explanation (self-NLE) to justify… (see more) 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.
2025-06-09
ArXiv (preprint)
doi.org
arxiv.org
NeuroFaith: Evaluating LLM Self-Explanation Faithfulness via Internal Representation Alignment
Milan Bhan
Jean-Noël Vittaut
Nicolas Chesneau
A. Chandar
Marie-Jeanne Lesot
Large Language Models (LLMs) can generate plausible free text self-explanations to justify their answers. However, these natural language ex… (see more)planations may not accurately reflect the model's actual reasoning process, pinpointing a lack of faithfulness. Existing faithfulness evaluation methods rely primarily on behavioral tests or computational block analysis without examining the semantic content of internal neural representations. This paper proposes NeuroFaith, a flexible framework that measures the faithfulness of LLM free text self-explanation by identifying key concepts within explanations and mechanistically testing whether these concepts actually influence the model's predictions. We show the versatility of NeuroFaith across 2-hop reasoning and classification tasks. Additionally, we develop a linear faithfulness probe based on NeuroFaith to detect unfaithful self-explanations from representation space and improve faithfulness through steering. NeuroFaith provides a principled approach to evaluating and enhancing the faithfulness of LLM free text self-explanations, addressing critical needs for trustworthy AI systems.
2025-06-09
ArXiv (preprint)
arxiv.org
arxiv.org