多个LLM之间的网络形成和动态
Marios Papachristou1,2, Yuan Yuan3
1Department of Information Systems, W.P. Carey School of Business, Arizona State University, 400 E Lemon St, Tempe, AZ 85281, USA.
PNAS nexus
|December 4, 2025
概括
大型语言模型 (LLM) 模仿人类社交网络的形成,在友和专业环境中复制关键原则. 这显示了LLMs.
科学领域:
- 社交网络分析分析
- 人工智能的人工智能是人工智能.
- 计算社会科学 计算社会科学
背景情况:
- 社交网络是人类互动,意见形成和集体组织的基础.
- 大型语言模型 (LLM) 越来越多地融入社会和专业环境.
- 了解LLM网络动态对于评估其社会影响至关重要.
研究的目的:
- 引入一个框架来研究多个LLM代理人的网络形成行为.
- 为了将LLM网络形成与人类决策进行基准测试.
- 探索LLM适应不同社会背景的适应性.
主要方法:
- 开发一个框架来分析LLM代理互动.
- 将LLM决策与人类链接形成数据进行比较.
- 在合成和现实世界的网络数据集之间进行测试 (友,电信,就业).
主要成果:
- 简单的LLM复制了核心微层网络原则 (优先附加,三位式关闭,同类性).
- 在LLM中,重复宏观网络属性 (社区结构,小世界效应).
- 法学士网络行为适应上下文,有利于友中的同性恋和组织环境中的异性恋.
结论:
- 法律学士表现出与人际网络形成原则的显著一致.
- 在社会模拟和合成数据生成方面,LLM具有潜力.
- 强调了人工智能与人类网络交互的偏见和公平性风险.
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