From Plausible to Actionable: A Position on LLM Self-Explanations

The paper discusses how Large Language Models can produce natural language self‑explanations that appear plausible but may not accurately reflect the model’s reasoning. It critiques current evaluation methods for such explanations and offers practical guidelines to assess their plausibility and faithfulness. Additionally, it argues that evaluation should also consider the actionability of these explanations, showing how they can aid decision‑making for various stakeholders.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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