This paper aims to investigate the open research problem of uncovering the social behaviors of LLM-based agents. To achieve this goal, we adopt Avalon, a representative communication game, as the environment and use system prompts to guide LLM agents to play the game. While previous studies have conducted preliminary investigations into gameplay with LLM agents, there lacks research on their social behaviors. In this paper, we present a novel framework designed to seamlessly adapt to Avalon gameplay. The core of our proposed framework is a multi-agent system that enables efficient communication and interaction among agents. We evaluate the performance of our framework based on metrics from two perspectives: winning the game and analyzing the social behaviors of LLM agents. Our results demonstrate the effectiveness of our framework in generating adaptive and intelligent agents and highlight the potential of LLM-based agents in addressing the challenges associated with dynamic social environment interaction. By analyzing the social behaviors of LLM agents from the aspects of both collaboration and confrontation, we provide insights into the research and applications of this domain.

翻译：本文旨在探索基于大语言模型（LLM）的主体社会行为这一开放性研究问题。为此，我们选取具有代表性的沟通游戏《阿瓦隆》作为实验环境，通过系统提示引导大语言模型主体参与游戏。尽管已有研究初步探索了大语言模型主体在游戏中的表现，但对其社会行为的研究仍属空白。本文提出一个创新框架，可无缝适配《阿瓦隆》游戏机制。该框架的核心是多主体系统，能够实现主体间的高效通信与交互。我们从游戏获胜与社会行为分析两个维度评估框架性能，实验结果证明了框架在生成自适应智能主体方面的有效性，并揭示了基于大语言模型的主体应对动态社会环境交互挑战的潜力。通过从协作与对抗双重维度分析大语言模型主体的社会行为，本研究为该领域的研究与应用提供了重要启示。