新闻
When Expert Disagreement Hurts: Prestige and Selectivity in LLM Decision Revision
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers found that language models often make worse decisions when revising based on expert disagreement, despite reversing answers more frequently.
- 为何重要
- Matters for engineers building AI systems that incorporate human feedback or expert review into decision pipelines, especially in high-stakes domains.
- 注意
- Reversal frequency is a poor indicator of accuracy improvement. Models respond to prestige cues like expert labels even when argument content is identical.
- agent
- llm
- language model
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