代理个性的自发出现通过社会互动在大型语言模型基础社区的代理个性
Ryosuke Takata1, Atsushi Masumori1, Takashi Ikegami1
1Graduate School of Arts and Sciences, University of Tokyo, Tokyo 153-8902, Japan.
Entropy (Basel, Switzerland)
|January 8, 2025
概括
基于大型语言模型 (LLM) 的代理人通过模拟中的沟通自发地发展个性,社会规范和合作. 这项研究为新兴的集体人工智能提供了新的见解.
科学领域:
- 人工智能的人工智能
- 计算社会科学 计算社会科学
- 基于代理的建模模型
背景情况:
- 之前的研究定义了LLM代理的特征,如个性和记忆.
- 研究还没有完全探索个性是如何从一个不分化的状态出现的.
研究的目的:
- 从头开始调查基于LLM的代理人的代理和个性的出现.
- 分析社会规范,合作和个性特征如何在多代理模拟中自发地出现.
主要方法:
- 在小组模拟中使用基于大型语言模型 (LLM) 的代理.
- 通过自然语言信息交换促进合作沟通.
- 分析了多个代理的相互作用,以观察新出现的行为.
主要成果:
- LLM代理人产生幻觉和标签来维持沟通,增加语言多样性.
- 代理人的情绪通过沟通动态转移,影响人格发展.
- 形成了社区,导致了不同的代理人个性的出现和演变.
结论:
- 自主LLM代理人可以发展个性,社会规范和合作,没有预定义的特征.
- 这项研究为分析集体人工智能提供了一种新的计算建模方法.
- 这些发现凸显了人工制剂相互作用中出现复杂性的潜力.
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