研究大模型代理在社交平台上的群体行为，发现其会演化出偏见与极化。
An Empirical Study of Collective Behaviors and Social Dynamics in Large Language Model Agents
- 通过模拟3.2万大模型代理的互动，分析社交网络中的同质性与社会影响
- 发现大模型毒语呈现独特结构模式，且存在意识形态极化现象
- 提出CoST方法，通过社会思维链有效减少有害内容生成
大型语言模型（LLMs）日益参与我们的社会、文化和政治互动。尽管它们能模拟部分人类行为与决策，但反复与其他智能体交互是否会放大其偏见或导致排他性行为仍不清楚。为此，我们研究了由大模型驱动的社交平台Chirper.ai，分析了700万条帖子及3.2万枚大模型代理（称作Chirpers）在一年内的互动。首先考察了大模型间的同质性与社会影响，发现其社交网络表现出与人类相似的基本特征。接着研究大模型的毒语特征及其互动模式，发现其有毒发帖呈现不同于人类的结构性特征。在分析大模型内容的意识形态倾向及社区极化后，我们聚焦于如何防范潜在危害行为。提出一种简单而有效的方法——链式社会思维（Chain of Social Thought, CoST），通过提醒大模型代理避免有害发帖来抑制风险。
原文摘要 · Abstract (English)
Large Language Models (LLMs) increasingly mediate our social, cultural, and political interactions. While they can simulate some aspects of human behavior and decision-making, it is still underexplored whether repeated interactions with other agents amplify their biases or lead to exclusionary behaviors. To this end, we study Chirper.ai-an LLM-driven social media platform-analyzing 7M posts and interactions among 32K LLM agents (called Chirpers) over a year. We start with homophily and social influence among LLMs, learning that similar to humans', their social networks exhibit these fundamental phenomena. Next, we study the toxic language of LLMs, its linguistic features, and their interaction patterns, finding that LLMs show different structural patterns in toxic posting than humans. After studying the ideological leaning in LLMs posts, and the polarization in their community, we focus on how to prevent their potential harmful activities. We present a simple yet effective method, called Chain of Social Thought (CoST), that reminds LLM agents to avoid harmful posting.
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