用多人辩论框架揭示大模型政治偏见的形成机制
Revealing Political Bias in LLMs through Structured Multi-Agent Debate
- 设计包含不同党派与性别的虚拟代理，模拟政治辩论场景
- 中立代理始终倾向民主党，共和党代理趋向中立，且态度随辩论推进强化
- 性别信息影响观点表达，同党派代理易形成意见回音室
大型语言模型（LLMs）被越来越多地用于模拟社会行为，但其在辩论中的政治偏见与互动动态仍缺乏深入研究。本文通过结构化多智能体辩论框架，让代表中立、共和党和民主党立场的美国大模型代理就敏感政治话题展开辩论。系统性地改变底层模型、代理性别和辩论形式，考察模型来源与代理人格对辩论过程中政治偏见与态度的影响。结果发现：中立代理始终倾向民主党，共和党代理逐渐向中立靠拢；性别会影响代理态度，当知晓对方性别时会调整自身立场；与以往研究相反，具有相同政治立场的代理会形成回音室效应，随着辩论进行观点进一步强化。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used to simulate social behaviour, yet their political biases and interaction dynamics in debates remain underexplored. We investigate how LLM type and agent gender attributes influence political bias using a structured multi-agent debate framework, by engaging Neutral, Republican, and Democrat American LLM agents in debates on politically sensitive topics. We systematically vary the underlying LLMs, agent genders, and debate formats to examine how model provenance and agent personas influence political bias and attitudes throughout debates. We find that Neutral agents consistently align with Democrats, while Republicans shift closer to the Neutral; gender influences agent attitudes, with agents adapting their opinions when aware of other agents' genders; and contrary to prior research, agents with shared political affiliations can form echo chambers, exhibiting the expected intensification of attitudes as debates progress.
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