The rapid advancement of chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming. This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents, and provides insight into their "cognitive" processes. To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named role-playing. Our approach involves using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of a society of agents, providing a valuable resource for investigating conversational language models. In particular, we conduct comprehensive studies on instruction-following cooperation in multi-agent settings. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond: https://github.com/camel-ai/camel.

翻译：基于聊天的语言模型的快速发展推动了复杂任务求解的显著进步。然而，其成功高度依赖人类输入的对话引导，这一过程既具挑战性又耗时。本文探索构建可扩展技术以促进沟通智能体自主协作的潜力，并深入洞察其“认知”过程。为应对实现自主协作的挑战，我们提出一种名为"角色扮演"的新型沟通智能体框架。该方法采用初始提示引导聊天智能体在保持与人类意图一致性的前提下完成任务执行。我们展示了角色扮演如何用于生成对话数据，以研究智能体社会的行为与能力，为探究对话语言模型提供宝贵资源。特别地，我们针对多智能体场景下的指令遵循协作开展了系统性研究。本文贡献包括：提出新型沟通智能体框架，提供研究多智能体系统协作行为与能力的可扩展方法，并开源支持沟通智能体及相关领域研究的代码库：https://github.com/camel-ai/camel。