用故事引导大模型合作，能显著提升协作成功率。
The Power of Stories: Narrative Priming Shapes How LLM Agents Collaborate and Compete
- 用团队主题故事对齐大模型，引导其选择合作策略。
- 共享同一故事时，协作成功率达78%，远超分歧故事组。
- 适合研究多智能体协同、AI对齐与叙事机制的学者。
根据尤瓦尔·诺亚·赫拉利的观点，大规模人类协作依赖于共同叙述所承载的共信与价值。本研究探究此类叙述是否也能引导大模型代理走向协作。我们采用有限重复的公共品博弈，让大模型代理在合作与利己支出间做出选择。通过不同强度的团队协作主题故事进行预置，测试其对谈判结果的影响。实验探讨四个问题：(1) 叙事如何影响谈判行为？(2) 代理共享相同故事与不同故事有何差异？(3) 代理数量增加时情况如何？(4) 代理是否能抵御自利型谈判者？结果表明，基于故事的预置显著影响策略与成功率。共享同一故事显著提升协作，使各方收益提高；而不同故事则逆转效果，自我导向预置的代理占据主导。我们推测这些发现对多智能体系统设计与人工智能对齐具有启示意义。
原文摘要 · Abstract (English)
According to Yuval Noah Harari, large-scale human cooperation is driven by shared narratives that encode common beliefs and values. This study explores whether such narratives can similarly nudge LLM agents toward collaboration. We use a finitely repeated public goods game in which LLM agents choose either cooperative or egoistic spending strategies. We prime agents with stories highlighting teamwork to different degrees and test how this influences negotiation outcomes. Our experiments explore four questions:(1) How do narratives influence negotiation behavior? (2) What differs when agents share the same story versus different ones? (3) What happens when the agent numbers grow? (4) Are agents resilient against self-serving negotiators? We find that story-based priming significantly affects negotiation strategies and success rates. Common stories improve collaboration, benefiting each agent. By contrast, priming agents with different stories reverses this effect, and those agents primed toward self-interest prevail. We hypothesize that these results carry implications for multi-agent system design and AI alignment.
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