In this article, we argue that understanding the collective behavior of agents based on large language models (LLMs) is an essential area of inquiry, with important implications in terms of risks and benefits, impacting us as a society at many levels. We claim that the distinctive nature of LLMs--namely, their initialization with extensive pre-trained knowledge and implicit social priors, together with their capability of adaptation through in-context learning--motivates the need for an interactionist paradigm consisting of alternative theoretical foundations, methodologies, and analytical tools, in order to systematically examine how prior knowledge and embedded values interact with social context to shape emergent phenomena in multi-agent generative AI systems. We propose and discuss four directions that we consider crucial for the development and deployment of LLM-based collectives, focusing on theory, methods, and trans-disciplinary dialogue.

翻译：本文认为，理解基于大语言模型（LLMs）的智能体集体行为是一个至关重要的研究领域，其潜在风险与效益具有重要社会意义，将在多个层面影响人类社会。我们主张，LLMs的独特性质——即通过大规模预训练知识及隐含社会先验进行初始化，并具备情境学习适应能力——促使我们需要建立一种互动主义范式，该范式包含替代性的理论基础、方法论与分析工具，以系统性地考察先验知识与内嵌价值如何与社会情境相互作用，从而塑造多智能体生成式AI系统中的涌现现象。我们提出并讨论了四个对LLM集体系统开发与部署至关重要的研究方向，重点关注理论构建、方法创新及跨学科对话。