新闻
Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers tested whether LLM explanations of decisions actually match the factors that drive those decisions using controlled interventions across Claude, GPT, and Gemini models.
- 为何重要
- Engineers building systems where users rely on LLM explanations to monitor, debug, or override decisions need to know if those explanations are trustworthy.
- 注意
- Cited factors often do not match measured influence; uncited factors sometimes score higher than cited ones, suggesting explanations may mislead operators about true decision drivers.
- agent
- llm
- eval
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