Scale-free networks are one of the most famous examples of emergent behavior and are ubiquitous in social systems, especially online social media in which users can follow each other. By analyzing the interactions of multiple generative agents using GPT3.5-turbo as a language model, we demonstrate their ability to not only mimic individual human linguistic behavior but also exhibit collective phenomena intrinsic to human societies, in particular the emergence of scale-free networks. We discovered that this process is disrupted by a skewed token prior distribution of GPT3.5-turbo, which can lead to networks with extreme centralization as a kind of alignment. We show how renaming agents removes these token priors and allows the model to generate a range of networks from random networks to more realistic scale-free networks.

翻译：无标度网络是涌现行为最著名的范例之一，普遍存在于社交系统中，尤其在用户可相互关注的在线社交媒体中。通过分析使用GPT3.5-turbo作为语言模型的多个生成型智能体的交互，我们证明了这些智能体不仅能够模仿个体人类语言行为，还能展现人类社会特有的集体现象，尤其是无标度网络的涌现。我们发现，GPT3.5-turbo非均衡的标记先验分布会破坏这一过程，可能导致网络出现极端中心化，这可以视为一种对齐现象。我们展示了如何通过对智能体重命名来消除这些标记先验，使模型能够生成从随机网络到更真实的无标度网络在内的多种网络结构。