LLM代理通过社交互动自发形成个性差异，揭示群体智能新机制。
Spontaneous Emergence of Agent Individuality through Social Interactions in LLM-Based Communities
- 代理在自然语言协作中通过互动自发产生个性
- 互动引发幻觉和话题标签，提升交流词汇多样性
- 适合研究群体智能与人工社会演化的学者
我们通过基于大语言模型（LLM）的代理群体模拟，研究从零开始涌现出的自主性。以往研究中，代理的性格与记忆通常预先设定；本文关注个体性（如行为、性格、记忆）如何从无差别状态中分化。实验中的LLM代理在群体内进行合作式自然语言交流，通过分析多代理仿真，发现社会规范、协作模式及人格特质可自发形成。代理在交流中产生幻觉与话题标签，从而增加交互词汇多样性；情绪随交流动态变化，随着社区形成，代理个性逐渐显现并演化。该计算建模方法为集体人工智能分析提供了新路径。
原文摘要 · Abstract (English)
We study the emergence of agency from scratch by using Large Language Model (LLM)-based agents. In previous studies of LLM-based agents, each agent's characteristics, including personality and memory, have traditionally been predefined. We focused on how individuality, such as behavior, personality, and memory, can be differentiated from an undifferentiated state. The present LLM agents engage in cooperative communication within a group simulation, exchanging context-based messages in natural language. By analyzing this multi-agent simulation, we report valuable new insights into how social norms, cooperation, and personality traits can emerge spontaneously. This paper demonstrates that autonomously interacting LLM-powered agents generate hallucinations and hashtags to sustain communication, which, in turn, increases the diversity of words within their interactions. Each agent's emotions shift through communication, and as they form communities, the personalities of the agents emerge and evolve accordingly. This computational modeling approach and its findings will provide a new method for analyzing collective artificial intelligence.
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