In this paper, we present a novel framework for enhancing the capabilities of large language models (LLMs) by leveraging the power of multi-agent systems. Our framework introduces a collaborative environment where multiple intelligent agent components, each with distinctive attributes and roles, work together to handle complex tasks more efficiently and effectively. We demonstrate the practicality and versatility of our framework through case studies in artificial general intelligence (AGI), specifically focusing on the Auto-GPT and BabyAGI models. We also examine the "Gorilla" model, which integrates external APIs into the LLM. Our framework addresses limitations and challenges such as looping issues, security risks, scalability, system evaluation, and ethical considerations. By modeling various domains such as courtroom simulations and software development scenarios, we showcase the potential applications and benefits of our proposed multi-agent system. Our framework provides an avenue for advancing the capabilities and performance of LLMs through collaboration and knowledge exchange among intelligent agents.

翻译：本文提出了一种新型框架，通过利用多智能体系统的力量来增强大型语言模型（LLM）的能力。该框架引入了一个协作环境，其中多个具有独特属性和角色的智能体组件共同高效处理复杂任务。通过人工通用智能（AGI）领域的案例研究，我们重点展示了Auto-GPT和BabyAGI模型的实用性和通用性，并分析了将外部API集成至LLM的“Gorilla”模型。我们的框架解决了循环问题、安全风险、可扩展性、系统评估及伦理考量等局限与挑战。通过模拟法庭审判、软件开发等多元场景，我们展示了所提多智能体系统的潜在应用与优势。该框架通过智能体间的协作与知识共享，为提升LLM的能力与性能提供了新的途径。