用AI模拟多人辩论，缓解观点偏见但难改变立场
Argumentative Experience: Reducing Confirmation Bias on Controversial Issues through LLM-Generated Multi-Persona Debates
- 用大模型生成多角色辩论，呈现对立观点
- 未显著增加用户关注反对意见，也未明显改变原有信念
- 能缓冲个体认知倾向带来的偏见，适合反思性信息探索
由大型语言模型驱动的多角色辩论系统在减少确认偏见方面展现出潜力，可缓解回音室效应与社会分化。然而，现有实证研究仍有限，尚不清楚其是否能有效引导用户关注挑战性观点、促进信念转变，或优于传统去偏策略。为此，我们对比了基于LLM的多角色辩论系统与双立场检索系统，在争议话题上向参与者展示多元视角。通过收集眼动数据、信念变化度量和定性反馈发现：该辩论系统虽未显著提升用户对相反观点的关注度，也未促使用户脱离原有信念，但能有效缓冲由个体认知倾向引发的偏见。这一结果揭示了多角色辩论系统在信息获取中的潜力与局限，并为未来设计更平衡、更具反思性的信息交互提供指导。
原文摘要 · Abstract (English)
Multi-persona debate systems powered by large language models (LLMs) show promise in reducing confirmation bias, which can fuel echo chambers and social polarization. However, empirical evidence remains limited on whether they meaningfully shift user attention toward belief-challenging content, promote belief change, or outperform traditional debiasing strategies. To investigate this, we compare an LLM-based multi-persona debate system with a two-stance retrieval-based system, exposing participants to multiple viewpoints on controversial topics. By collecting eye-tracking data, belief change measures, and qualitative feedback, our results show that while the debate system does not significantly increase attention to opposing views, or make participants shift away from prior beliefs, it does provide a buffering effect against bias caused by individual cognitive tendency. These findings shed light on both the promise and limits of multi-persona debate systems in information seeking, and we offer design insights to guide future work toward more balanced and reflective information engagement.
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