arXiv · 2312.06619
Emergence of Scale-Free Networks in Social Interactions among Large Language Models
Abstract
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.
Explore related subjects
Keep this discovery
Giordano De Marzo, Luciano Pietronero, David Garcia. 2023-12-11. Emergence of Scale-Free Networks in Social Interactions among Large Language Models. https://arxiv.org/abs/2312.06619
Cite the original work for its findings. Save a collection to share your selection of sources.