不同提示框架影响独立大模型决策，揭示非交互场景下的认知偏差。
Framing Effects in Independent-Agent Large Language Models: A Cross-Family Behavioral Analysis
- 通过不同表述的提示测试模型选择倾向
- 框架差异导致偏好向保守选项转移
- 适用于研究提示设计与多智能体对齐
在许多真实应用中，大语言模型作为独立智能体运行而无法互动，从而限制了协同。本文在无交互条件下，考察提示框架如何影响涉及个体与群体利益冲突的阈值投票任务中的决策行为。对多种大模型家族在孤立实验中测试了两种逻辑等价但表述不同的提示。结果显示，提示框架显著影响选择分布，常使偏好转向风险规避选项；表面语言线索甚至可覆盖逻辑等价性。这表明，当成功需承担风险时，模型行为更倾向于工具理性而非合作理性。研究揭示了非交互多智能体部署中提示框架效应是重要偏差来源，对对齐与提示设计具有启示意义。
原文摘要 · Abstract (English)
In many real-world applications, large language models (LLMs) operate as independent agents without interaction, thereby limiting coordination. In this setting, we examine how prompt framing influences decisions in a threshold voting task involving individual-group interest conflict. Two logically equivalent prompts with different framings were tested across diverse LLM families under isolated trials. Results show that prompt framing significantly influences choice distributions, often shifting preferences toward risk-averse options. Surface linguistic cues can even override logically equivalent formulations. This suggests that observed behavior reflects a tendency consistent with a preference for instrumental rather than cooperative rationality when success requires risk-bearing. The findings highlight framing effects as a significant bias source in non-interacting multi-agent LLM deployments, informing alignment and prompt design.
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