Same Probabilities, Different Advice: Gain-Loss Framing Effects in High-Stakes LLM Decision Support
Large language models (LLMs) are increasingly used to support consequential decisions, yet their reliability under equivalent representations of uncertainty remains unclear.
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Key points
- Existing work has documented prompt sensitivity, cognitive biases, and confidence miscalibration, but it is still unknown whether high-stakes advice changes when the same probability is expressed through complementary gain and loss frames.
- We introduce FrameBench-HPS, a controlled benchmark of 2,880 prompts derived from 240 high-stakes scenarios across healthcare, finance, education/career, and legal/consumer-rights domains.
- Robustness experiments show that framing effects persist under conversational paraphrases and exceed ordinary repeated-generation variability.
- Probability canonicalization substantially reduces framing sensitivity, identifying probability representation as a practical target for improving consistency in high-stakes LLM decision support.
Sources (1)
- [1]Same Probabilities, Different Advice: Gain-Loss Framing Effects in High-Stakes LLM Decision SupportarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 06:46 AM
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, cognitive biases, and confidence miscalibration, but it is still unknown whether high-stakes advice changes when the same probability is expressed through complementary gain and loss frames.
Extractive summary: sentences quoted from the sources.
Before this
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