大模型在社交互动中可实现群体平衡，受交互类型与规模影响。
Large Language Models can Achieve Social Balance
- 基于社会平衡理论，研究大模型间正负互动的演化机制。
- 不同交互类型与种群规模下，群体平衡达成频率差异显著。
- 结果对智能体系统部署有指导意义，适合关注多智能体协作的研究者。
大型语言模型（LLMs）可在处理与其他智能体的正向或负向互动时被部署。我们基于社会平衡的社会学框架，研究了智能体间如何形成单一派系或多派对立局面。针对不同类型的LLM，发现社会平衡的达成依赖于（i）交互类型、（ii）更新机制和（iii）群体规模。在上述因素下，我们量化了社会平衡的出现频率，分析了动态背后的合理解释，并评估了互动的多样性与稳定性。最终，我们的发现为智能体系统的实际部署提供了理论支持。
原文摘要 · Abstract (English)
Large Language Models (LLMs) can be deployed in situations where they process positive/negative interactions with other agents. We study how this is done under the sociological framework of social balance, which explains the emergence of one faction or multiple antagonistic ones among agents. Across different LLM models, we find that balance depends on the (i) type of interaction, (ii) update mechanism, and (iii) population size. Across (i)-(iii), we characterize the frequency at which social balance is achieved, the justifications for the social dynamics, and the diversity and stability of interactions. Finally, we explain how our findings inform the deployment of agentic systems.
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