Did I Faithfully Say What I Thought? Bridging the Gap Between Neural Activity and Self-Explanations in Large Language Models

• URL: http://arxiv.org/abs/2506.09277v2
• Date: Thu, 12 Jun 2025 13:30:28 GMT
• Title: Did I Faithfully Say What I Thought? Bridging the Gap Between Neural Activity and Self-Explanations in Large Language Models
• Authors: Milan Bhan, Jean-Noel Vittaut, Nicolas Chesneau, Sarath Chandar, Marie-Jeanne Lesot,
• Abstract summary: Large Language Models (LLM) have demonstrated the capability of generating free text self Natural Language Explanation (self-NLE) to justify their answers.<n>This work introduces a novel flexible framework for quantitatively measuring the faithfulness of LLM-generated self-NLE.<n>The proposed framework is versatile and provides deep insights into self-NLE faithfulness by establishing a direct connection between self-NLE and model reasoning.
• Score: 9.499055857747322
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLM) have demonstrated the capability of generating free text self Natural Language Explanation (self-NLE) to justify 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.

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