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LLM advice shifts significantly with gain-loss framing in high-stakes scenarios

By PulseAugur Editorial · [1 sources] ·

A new benchmark called FrameBench-HPS has been developed to test how large language models (LLMs) provide advice in high-stakes situations across healthcare, finance, education, and legal domains. The benchmark revealed that LLMs change their recommendations in over 32% of cases when the same probability is presented using gain-loss framing. Human evaluations confirmed that these framing effects alter the perceived risk and directiveness of the LLM's advice, even persisting through conversational paraphrasing. The study suggests that standardizing probability representations could significantly improve the consistency of LLM decision support in critical applications. AI

IMPACT Highlights the need for improved consistency in LLM decision support for critical applications.

RANK_REASON The cluster contains a research paper detailing a new benchmark for evaluating LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM advice shifts significantly with gain-loss framing in high-stakes scenarios

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The cluster contains a research paper detailing a new benchmark for evaluating LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yuyao Zhang, Yakun Cui, Han Zhu, Jinsong Lu, Yike Guo, Sirui Han ·

    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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