Large Language Models (LLMs) have revolutionized Natural Language Processing but exhibit limitations, particularly in autonomously addressing novel challenges such as reasoning and problem-solving. Traditional techniques like chain-of-thought prompting necessitate explicit human guidance. This paper introduces a novel multi-agent communication framework, inspired by the CAMEL model, to enhance LLMs' autonomous problem-solving capabilities. The framework employs multiple LLM agents, each with a distinct persona, engaged in role-playing communication, offering a nuanced and adaptable approach to diverse problem scenarios. Extensive experimentation demonstrates the framework's superior performance and adaptability, providing valuable insights into the collaborative potential of multiple agents in overcoming the limitations of individual models.

翻译：大型语言模型（LLMs）虽已彻底革新自然语言处理领域，但在自主应对推理与问题求解等新挑战时仍存在局限性。传统方法如思维链提示需要明确的人类引导。本文受CAMEL模型启发，提出一种新颖的多智能体通信框架以增强LLM的自主问题解决能力。该框架部署多个具有差异化角色的LLM智能体，通过角色扮演通信机制实现对多样化问题场景的精细化自适应处理。大量实验表明，该框架在性能与适应性方面表现卓越，为通过多智能体协同突破单模型局限性提供了重要启示。