Accurately modeling opinion change through social interactions is crucial for understanding and mitigating polarization, misinformation, and societal conflict. Recent work simulates opinion dynamics with role-playing LLM agents (RPLAs), but multi-agent simulations often display unnatural group behavior (e.g., premature convergence) and lack empirical benchmarks for assessing alignment with real human group interactions. We introduce DEBATE, a large-scale benchmark for evaluating the authenticity of opinion dynamics in multi-agent RPLA simulations. DEBATE contains 36,383 messages from 2,832 U.S.-based participants across 708 groups and 107 topics, with both public messages and private Likert-scale beliefs, enabling evaluation at the utterance and group levels (and supporting future individual-level analyses). We instantiate "digital twin" RPLAs with seven LLMs and evaluate across two settings: next-message prediction and full conversation rollout, using stance-alignment and opinion-convergence metrics. In zero-shot settings, RPLA groups exhibit strong opinion convergence relative to human groups. Post-training via supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) improves stance alignment and brings group-level convergence closer to human behavior, though discrepancies in opinion change and belief updating remain. DEBATE enables rigorous benchmarking of simulated opinion dynamics and supports future research on aligning multi-agent RPLAs with realistic human interactions.

翻译：通过社会互动准确建模观点变化对于理解和缓解极化、错误信息及社会冲突至关重要。近期研究利用角色扮演大语言模型智能体（RPLAs）模拟观点动态，但多智能体模拟常表现出非自然的群体行为（如过早收敛），且缺乏评估其与真实人类群体互动一致性的实证基准。本文提出DEBATE——一个用于评估多智能体RPLA模拟中观点动态真实性的大规模基准。DEBATE包含来自708个小组、107个议题的2,832名美国参与者的36,383条消息，涵盖公共消息和私人李克特量表信念数据，支持在话语层面和群体层面进行评估（并为未来个体层面分析提供基础）。我们使用七种大语言模型实例化“数字孪生”RPLAs，通过立场对齐和观点收敛指标，在两种设定下进行评估：下一条消息预测和完整对话推演。在零样本设定中，RPLA群体相较于人类群体表现出强烈的观点收敛倾向。通过监督微调（SFT）和直接偏好优化（DPO）进行后训练后，立场对齐得到改善，群体层面收敛行为更接近人类，但在观点变化和信念更新方面仍存在差异。DEBATE为模拟观点动态提供了严谨的基准测试框架，并支持未来关于使多智能体RPLAs与真实人类互动对齐的研究。