基于LLM的社区中通过社会交互自发涌现的智能体个体性
Spontaneous Emergence of Agent Individuality through Social Interactions in LLM-Based Communities
AI总结：
本研究利用LLM智能体群体模拟，发现智能体通过自然语言合作交流，能自发涌现出社会规范、合作、个性及个体性差异，并产生幻觉和标签以维持沟通，为集体AI分析提供新方法。
摘要：
我们通过使用基于大型语言模型（LLM）的智能体，从零开始研究能动性的涌现。在以往关于基于LLM的智能体研究中，每个智能体的特征（包括个性和记忆）传统上都是预先定义的。我们关注的是行为、个性和记忆等个体性如何从未分化状态中分化出来。本LLM智能体在群体模拟中进行合作性交流，以自然语言交换基于上下文的消息。通过分析这一多智能体模拟，我们报告了关于社会规范、合作和人格特质如何自发涌现的宝贵新见解。本文证明，自主交互的LLM驱动智能体会产生幻觉和标签以维持交流，这反过来增加了其交互中词汇的多样性。每个智能体的情绪通过交流而转变，随着它们形成社区，智能体的人格也随之涌现和演化。这种计算建模方法及其发现将为分析集体人工智能提供一种新方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
