English(EN) Same Probabilities, Different Advice: Gain-Loss Framing Effects in High-Stakes LLM Decision Support

在高风险情境下，LLM的建议会因增益-损失框架而发生显著变化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 04:00

开发了一个名为FrameBench-HPS的新基准，用于测试大型语言模型（LLM）在医疗、金融、教育和法律等领域的高风险情境下提供建议的方式。该基准显示，当使用增益-损失框架呈现相同概率时，LLM的推荐在超过32%的情况下会发生改变。人类评估证实，即使经过对话释义，这些框架效应也会改变LLM建议的感知风险和指令性。研究表明，标准化概率表示可以显著提高LLM在关键应用中决策支持的一致性。 AI

影响强调了提高LLM在关键应用中决策支持一致性的必要性。

排序理由该集群包含一篇研究论文，详细介绍了用于评估LLM行为的新基准。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Yuyao Zhang, Yakun Cui, Han Zhu, Jinsong Lu, Yike Guo, Sirui Han · 2026-10-22 04:00

Same Probabilities, Different Advice: Gain-Loss Framing Effects in High-Stakes LLM Decision Support

arXiv:2610.29294v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to support consequential decisions, yet their reliability under equivalent representations of uncertainty remains unclear. Existing work has documented prompt sensitivity, cogniti…

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Same Probabilities, Different Advice: Gain-Loss Framing Effects in High-Stakes LLM Decision Support

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