发表机构
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究引入DecisionQE框架，以狼人杀为测试平台，发现顺从倾向强的LLM在群体决策中合作优势更稳定，且顺从兼具支持合作与提升对抗角色隐藏能力的双重效应，为LLM安全评估提供了新视角。
AI 中文摘要
大型语言模型（LLMs）正越来越多地参与到与其他LLMs及人类共同进行的群体决策中，但目前仍不清楚它们的影响力是由面向说服的表达还是面向顺从的迁就所驱动。我们引入了DecisionQE，一种基于问卷的框架，用于在多个决策场景中测量每个模型的说服倾向与顺从倾向，并以狼人杀游戏作为交互式测试平台，研究在非对称信息下它们对社会影响力及群体结果的作用。在所有实验中，更强的说服倾向并未显著提升群体结果，而面向顺从的模型在合作中展现出更稳定的优势。我们进一步揭示了顺从的双重效应：它在诚实角色中支持合作，却在对抗性角色中提升隐藏能力。这些发现表明，LLM的群体交互不仅能反映任务结果，还能揭示可测量的内在行为倾向模式。因此，LLMs可作为观察语言介导交互的社会学视角，同时强调需将行为倾向纳入LLM系统的安全评估。
英文摘要
Large language models (LLMs) are increasingly involved in group decision-making with other LLMs and humans. Yet it remains unclear whether their influence is driven by persuasion-oriented expression or compliance-oriented accommodation. We introduce DecisionQE, a questionnaire-based framework for measuring each model's persuasive and compliant tendencies across multiple decision scenarios, and use the Werewolf game as an interactive testbed to study their effects on social influence and group outcomes under asymmetric information. Across experiments, stronger persuasive tendency does not significantly improve group outcomes, whereas compliant-oriented models show more stable advantages in cooperation. We further reveal a dual effect of compliance: it supports cooperation in honest roles but improves concealment in adversarial roles. These findings suggest that LLM group interactions reveal not only task outcomes, but also measurable patterns of intrinsic behavioral tendency. LLMs can therefore serve as a lens for sociological observation of language-mediated interaction, while highlighting the need to incorporate behavioral tendencies into safety evaluation of LLM systems.
