The rapid advancement of conversational and 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 provide 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 chat agents, providing a valuable resource for investigating
conversational language models. 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. The GitHub repository of this project is made publicly available on:
this https URL

本文提出了一种名为 “角色扮演” 的新型交际代理框架来实现自主协作，使用初始提示指导聊天代理完成任务并保持与人类意图的一致性，其贡献在于介绍了一种新颖的交际代理框架，提供了一种可扩展的研究多智能体系统合作行为和能力的方法，并开源了实现支持研究的库。

CAMEL: 大规模语言模型社群 “心智” 探索的沟通代理

CAMEL: Communicative Agents for "Mind" Exploration of Large Scale  Language Model Society

A distinguishing property of human intelligence is the ability to flexibly
use language in order to communicate complex ideas with other humans in a
variety of contexts. Research in natural language dialogue should focus on
designing communicative agents which can integrate themselves into these
contexts and productively collaborate with humans. In this abstract, we propose
a general situated language learning paradigm which is designed to bring about
robust language agents able to cooperate productively with humans.

本文提出一种通用的情境语言学习范式，旨在设计可靠的语言代理以能够与人类进行成功的合作。