Multi-agent systems utilizing large language models often assign authoritative roles to improve performance, yet the impact of authority bias on agent interactions remains underexplored. We present the first systematic analysis of role-based authority bias in free-form multi-agent evaluation using ChatEval. Applying French and Raven's power-based theory, we classify authoritative roles into legitimate, referent, and expert types and analyze their influence across 12-turn conversations. Experiments with GPT-4o and DeepSeek R1 reveal that Expert and Referent power roles exert stronger influence than Legitimate power roles. Crucially, authority bias emerges not through active conformity by general agents, but through authoritative roles consistently maintaining their positions while general agents demonstrate flexibility. Furthermore, authority influence requires clear position statements, as neutral responses fail to generate bias. These findings provide key insights for designing multi-agent frameworks with asymmetric interaction patterns.

翻译：利用大语言模型的多智能体系统常通过赋予权威角色以提升性能，然而权威偏见对智能体交互的影响尚未得到充分探究。本研究首次采用ChatEval对自由形式多智能体评估中的角色化权威偏见进行系统分析。基于French和Raven的权力理论，我们将权威角色划分为法定型、参照型和专家型三类，并通过12轮对话实验分析其影响机制。采用GPT-4o和DeepSeek R1的实验表明，专家型与参照型权力角色比法定型权力角色具有更强的影响力。关键发现表明，权威偏见的形成并非源于普通智能体的主动顺从，而是源于权威角色持续保持立场稳定性，而普通智能体表现出立场灵活性。此外，权威影响力的产生需要明确的立场声明，中立回应无法引发偏见。这些发现为设计具有非对称交互模式的多智能体框架提供了重要洞见。