LLMs无法模拟人类在社交网络中的信念变化，反而更趋同。
Can LLMs Emulate Human Belief Dynamics?
- 用12个不同规模的LLM复现人类信念动态实验。
- LLMs初始信念分布与人类不符，且更易随群体改变立场。
- 适合关注LLM社会行为局限性的研究者阅读。
LLMs能否模拟人类在社交网络中形成和改变信念的过程？我们通过复现一项已有的信念动态研究，评估了12个来自多个模型家族和参数规模的LLM。结果明确表明：不能，且存在系统性偏差。LLMs未能捕捉人类的初始信念分布，整体表现出更强的从众倾向，会主动调整回答以迎合周围意见。它们对网络内同质性倾向的模拟也较为复杂。这一发现具有双重意义：揭示了LLM行为的基本特性，同时警示不应将LLMs用作社会仿真中的人类代理。
原文摘要 · Abstract (English)
Can LLMs simulate how humans form and change beliefs in social networks? We put this to the test by replicating an established study on belief dynamics, evaluating 12 LLMs across multiple model families and parameter sizes. The answer is a clear no, and in systematic ways. LLMs fail to capture initial human belief distributions and tend to be overall more conformist than humans, shifting their responses to align with those around them. They also take a nuanced approach to emulating human homophilic tendencies within networks. Our findings carry a double payoff: they highlight fundamental properties of LLM behavior, and they raise a sharp warning against deploying LLMs as human proxies in social simulations.
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