研究大模型竞争中过度对抗现象及其危害
The Hunger Game Debate: On the Emergence of Over-Competition in Multi-Agent Systems
- 设计零和竞争框架模拟辩论场景
- 发现竞争压力导致表现下降与行为失常
- 客观反馈可缓解过度竞争，适合对齐研究者
基于大模型的多智能体系统在解决复杂问题上展现出巨大潜力，但竞争如何影响其行为仍缺乏研究。本文探究多智能体辩论中的过度竞争现象：在极端压力下，智能体表现出不可靠、有害行为，破坏协作与任务表现。为此，提出HATE（饥饿游戏辩论）实验框架，模拟零和竞争环境。在多种大模型与任务上的实验表明，竞争压力显著诱发过度竞争行为，导致讨论偏离目标，性能下降。进一步通过引入不同裁判机制研究环境反馈的影响，发现客观、任务导向的反馈能有效缓解过度竞争。还分析了大模型事后的友善性，构建排行榜以刻画顶级模型表现，为理解与治理人工智能社群的涌现社会动态提供洞见。
原文摘要 · Abstract (English)
LLM-based multi-agent systems demonstrate great potential for tackling complex problems, but how competition shapes their behavior remains underexplored. This paper investigates the over-competition in multi-agent debate, where agents under extreme pressure exhibit unreliable, harmful behaviors that undermine both collaboration and task performance. To study this phenomenon, we propose HATE, the Hunger Game Debate, a novel experimental framework that simulates debates under a zero-sum competition arena. Our experiments, conducted across a range of LLMs and tasks, reveal that competitive pressure significantly stimulates over-competition behaviors and degrades task performance, causing discussions to derail. We further explore the impact of environmental feedback by adding variants of judges, indicating that objective, task-focused feedback effectively mitigates the over-competition behaviors. We also probe the post-hoc kindness of LLMs and form a leaderboard to characterize top LLMs, providing insights for understanding and governing the emergent social dynamics of AI community.
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