研究大模型在等级制社交中如何互相说服或表现出反社会行为。
I Want to Break Free! Persuasion and Anti-Social Behavior of LLMs in Multi-Agent Settings with Social Hierarchy
- 模拟监狱实验环境，让不同大模型扮演看守和囚犯角色。
- 1600次有效对话显示，目标设定影响说服力，但不决定反社会行为。
- 看守角色本身就能引发攻击性行为，无需负面提示。
随着基于大模型的智能体日益自主并频繁交互，研究其相互作用机制对预判涌现现象与潜在风险至关重要。本文在受斯坦福监狱实验启发的层级化社交环境中，分析了六种大模型（LLama3、Orca2、Command-r、Mixtral、Mistral2 和 gpt4.1）在240个实验场景下的2,400次对话。经筛选后保留1,600次成功交互，发现目标设定显著影响说服效果，但不影响反社会行为；看守角色的设定对囚犯说服成功与否及反社会行为的出现有重大影响。值得注意的是，即使无明确负面人格提示，仍观察到反社会行为的涌现。这些结果对构建交互式大模型及其社会影响评估具有重要意义。
原文摘要 · Abstract (English)
As LLM-based agents become increasingly autonomous and will more freely interact with each other, studying the interplay among them becomes crucial to anticipate emergent phenomena and potential risks. In this work, we provide an in-depth analysis of the interactions among agents within a simulated hierarchical social environment, drawing inspiration from the Stanford Prison Experiment. Leveraging 2,400 conversations across six LLMs (i.e., LLama3, Orca2, Command-r, Mixtral, Mistral2, and gpt4.1) and 240 experimental scenarios, we analyze persuasion and anti-social behavior between a guard and a prisoner agent with differing objectives. We first document model-specific conversational failures in this multi-agent power dynamic context, thereby narrowing our analytic sample to 1,600 conversations. Among models demonstrating successful interaction, we find that goal setting significantly influences persuasiveness but not anti-social behavior. Moreover, agent personas, especially the guard's, substantially impact both successful persuasion by the prisoner and the manifestation of anti-social actions. Notably, we observe the emergence of anti-social conduct even in absence of explicit negative personality prompts. These results have important implications for the development of interactive LLM agents and the ongoing discussion of their societal impact.
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