aiqbench analysis
This opinion paper advances the field by shifting focus from merely assessing the plausibility or faithfulness of Large Language Model self-explanations to evaluating their practical utility in driving informed decision-making. The authors propose new guidelines that extend traditional XAI metrics to include "actionability," addressing a critical gap between theoretical interpretability and real-world application for diverse stakeholders.
From Plausible to Actionable: A Position on LLM Self-Explanations
arXiv:2607.15957v1 Announce Type: new Abstract: Large Language Models (LLMs) can generate natural language explanations that rationalize their own decisions, a phenomenon commonly referred to as self-explanations.Such explanations have emerged as a promising direction for explainable artificial intelligence (XAI), particularly for interpreting LLM behavior.However, while self-explanations often appear plausible, whether they faithfully reflect a model's underlying reasoning process remains an open question. In this opinion paper, we argue that self-explanations can be highly plausible, questionably faithful, and yet highly actionable. From a traditional XAI perspective, we identify the limitations of standard evaluation protocols for LLM-generated self-explanations and propose practical guidelines for assessing their plausibility and faithfulness. Moreover, we argue that evaluation should extend beyond these criteria to actionability, highlighting applications of LLM rationalization capabilities that support informed decision-making and appropriate action across diverse stakeholders.
Abstract:Large Language Models (LLMs) can generate natural language explanations that rationalize their own decisions, a phenomenon commonly referred to as this http URL explanations have emerged as a promising direction for explainable artificial intelligence (XAI), particularly for interpreting LLM this http URL, while self-explanations often appear plausible, whether they faithfully reflect a model's underlying reasoning process remains an open question. In this opinion paper, we argue that self-explanations can be highly plausible, questionably faithful, and yet highly actionable. From a traditional XAI perspective, we identify the limitations of standard evaluation protocols for LLM-generated self-explanations and propose practical guidelines for assessing their plausibility and faithfulness. Moreover, we argue that evaluation should extend beyond these criteria to actionability, highlighting applications of LLM rationalization capabilities that support informed decision-making and appropriate action across diverse stakeholders.
Computation and Language (cs.CL)
arXiv:2607.15957 [cs.CL]
(or arXiv:2607.15957v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.15957 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 227 words
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