标题： 大型语言模型代理中类人类极化的出现 显示英文标题

标题： Emergence of human-like polarization among large language model agents

摘要： 大型语言模型（LLMs）的迅速发展不仅使自主代理能够生成社交网络、进行交流并在政治问题上形成共享和分歧的观点，还开始在塑造人类政治讨论中发挥日益重要的作用。 然而，我们对其集体行为和潜在机制的理解仍然不完整，这给人类社会带来了意想不到的风险。 在本文中，我们模拟了一个涉及数千个大型语言模型代理的网络系统，发现它们通过LLM对话引导的社会互动导致了类似人类的极化现象。 我们发现这些代理自发地形成了具有类似人类特性的社交网络，包括同质性聚类，同时也通过现实世界中观察到的机制，如回音室效应，塑造了它们的集体观点。 人类与LLM代理之间的相似性——包括行为、机制和涌现现象——引发了关于它们放大社会极化的潜力的担忧，但也有可能作为识别缓解极化及其后果的可行策略的重要测试平台。 显示英文摘要

摘要： Rapid advances in large language models (LLMs) have not only empowered autonomous agents to generate social networks, communicate, and form shared and diverging opinions on political issues, but have also begun to play a growing role in shaping human political deliberation. Our understanding of their collective behaviours and underlying mechanisms remains incomplete, however, posing unexpected risks to human society. In this paper, we simulate a networked system involving thousands of large language model agents, discovering their social interactions, guided through LLM conversation, result in human-like polarization. We discover that these agents spontaneously develop their own social network with human-like properties, including homophilic clustering, but also shape their collective opinions through mechanisms observed in the real world, including the echo chamber effect. Similarities between humans and LLM agents -- encompassing behaviours, mechanisms, and emergent phenomena -- raise concerns about their capacity to amplify societal polarization, but also hold the potential to serve as a valuable testbed for identifying plausible strategies to mitigate polarization and its consequences.