用大模型模拟社交影响，发现推理强的模型更不易受群体影响。
Towards Simulating Social Influence Dynamics with LLM-based Multi-agents
- 构建基于大模型的多智能体框架，模拟线上论坛社交行为
- 小模型更易从众，强推理模型更抗社会影响
- 适合对社会影响、群体行为建模感兴趣的读者
大型语言模型的进展为模拟复杂的人类社交互动提供了可能。我们研究了基于大模型的多智能体模拟是否能复现在线论坛中观察到的核心人类社交动态。通过结构化模拟框架，评估了不同模型规模和推理能力下的从众、群体极化与分化现象。结果表明，小模型表现出更高的从众率，而经过推理优化的模型则更能抵抗社会影响。
原文摘要 · Abstract (English)
Recent advancements in Large Language Models offer promising capabilities to simulate complex human social interactions. We investigate whether LLM-based multi-agent simulations can reproduce core human social dynamics observed in online forums. We evaluate conformity dynamics, group polarization, and fragmentation across different model scales and reasoning capabilities using a structured simulation framework. Our findings indicate that smaller models exhibit higher conformity rates, whereas models optimized for reasoning are more resistant to social influence.
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