让大模型代理通过学习形成类似人类的社交关系。
Learning to Make Friends: Coaching LLM Agents toward Emergent Social Ties
- 用奖励机制引导大模型代理模仿人类社交行为。
- 代理间形成稳定互动模式，网络结构接近真实社区。
- 适合研究人工智能群体行为与社会演化。
大型语言模型（LLM）代理能否再现人类在线行为中的复杂社交动态——如同质性、互惠性与社会认同？我们构建了一个多代理LLM模拟框架，其中代理通过重复互动、相互评价，并在上下文学习基础上接受教练信号加速适应。为模拟人类社交行为，设计了包含社交互动、信息获取、自我呈现、协作与情感支持等核心驱动力的奖励函数，使代理目标与真实用户动机对齐。实验表明，经教练引导的LLM代理发展出稳定的互动模式并形成涌现的社会关系，生成的网络结构与真实在线社区特性高度相似。该框架结合行为奖励与上下文适应，提供了一个系统化测试平台，用于研究LLM群体中的集体动态，并揭示人工代理如何逼近或偏离人类社交行为。
原文摘要 · Abstract (English)
Can large language model (LLM) agents reproduce the complex social dynamics that characterize human online behavior -- shaped by homophily, reciprocity, and social validation -- and what memory and learning mechanisms enable such dynamics to emerge? We present a multi-agent LLM simulation framework in which agents repeatedly interact, evaluate one another, and adapt their behavior through in-context learning accelerated by a coaching signal. To model human social behavior, we design behavioral reward functions that capture core drivers of online engagement, including social interaction, information seeking, self-presentation, coordination, and emotional support. These rewards align agent objectives with empirically observed user motivations, enabling the study of how network structures and group formations emerge from individual decision-making. Our experiments show that coached LLM agents develop stable interaction patterns and form emergent social ties, yielding network structures that mirror properties of real online communities. By combining behavioral rewards with in-context adaptation, our framework establishes a principled testbed for investigating collective dynamics in LLM populations and reveals how artificial agents may approximate or diverge from human-like social behavior.
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