Modeling opinion change through social interactions is important for understanding 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 benchmark for evaluating the authenticity of opinion dynamics in multi-agent RPLA simulations. DEBATE contains multi-round public messages and private Likert-scale beliefs from U.S.-based participants across 107 topics; the cleaned benchmark used in our experiments contains 2,788 participants in 697 groups, enabling evaluation at the utterance and group levels. We instantiate digital-twin RPLAs with seven LLMs and evaluate across two settings: next-message prediction and full dynamics simulation, using stance-based opinion-dynamics metrics. In zero-shot settings, RPLA groups exhibit strong opinion convergence relative to human groups. Across group-generalization tests with unseen participant groups and topic-generalization tests with unseen discussion topics, supervised fine-tuning (SFT) for Llama-3.1-8B-Instruct improves stance prediction of individuals and convergence prediction at the group level, though discrepancies in opinion change and belief updating remain. The dataset and benchmark website are publicly available.

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