Large Language Models (LLMs) have demonstrated an unprecedented ability to simulate human-like social behaviors, making them useful tools for simulating complex social systems. However, it remains unclear to what extent these simulations can be trusted to accurately capture key social mechanisms, particularly in highly unbalanced contexts involving minority groups. This paper uses a network generation model with controlled homophily and class sizes to examine how LLM agents behave collectively in multi-round debates. Moreover, our findings highlight a particular directional susceptibility that we term \textit{agreement drift}, in which agents are more likely to shift toward specific positions on the opinion scale. Overall, our findings highlight the need to disentangle structural effects from model biases before treating LLM populations as behavioral proxies for human groups.

翻译：大型语言模型（LLMs）展现了模拟类人社交行为的空前能力，使其成为模拟复杂社会系统的有效工具。然而，这些模拟在多大程度上能准确捕捉关键社会机制仍不明确，尤其是在涉及少数群体的高度非均衡情境下。本文采用一种基于可控同质性与群体规模参数的网络生成模型，探究LLM智能体在多轮辩论中的群体行为模式。此外，我们发现一种特定方向性的倾向，我们将其称为"意见漂移"（agreement drift），即智能体更易向意见谱系中的特定立场偏移。总体而言，我们的研究结果强调：在将LLM群体视为人类群体的行为代理模型之前，必须厘清结构效应与模型偏差的相互影响。