语言模型智能体的自适应会话内团队构建
Adaptive In-conversation Team Building for Language Model Agents
AI总结：
针对如何为特定任务有效构建LLM智能体团队的问题，提出名为Captain Agent的自适应团队构建范式，通过动态嵌套群组对话和反思管理团队，在六个场景中平均准确率提升21.94%，且无需任务特定提示工程。
摘要：
利用多个大型语言模型（LLM）智能体已被证明是处理复杂任务的一种有前景的方法，然而为特定应用有效设计多个智能体仍是一门艺术。因此，回答一个关键问题令人感兴趣：给定一个任务，我们如何构建一个LLM智能体团队来有效地解决它？我们新的自适应团队构建范式提供了一个灵活的解决方案，通过一种名为Captain Agent的新型智能体设计来实现。它通过利用嵌套的群组对话和反思，为任务解决过程的每一步动态组建和管理团队，以确保专业知识的多样性并防止刻板输出，从而允许采用灵活而结构化的问题解决方法。在六个真实世界场景中的全面评估表明，Captain Agent显著优于现有的多智能体方法，平均准确率提高了21.94%，在无需特定任务提示工程的情况下提供了出色的性能。我们对不同骨干LLM和成本分析的进一步探索表明，Captain Agent能够提高弱LLM的对话质量，并以极低的成本实现具有竞争力的性能，这为多智能体系统的应用提供了启示。
英文摘要：
Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks, while the effective design of multiple agents for a particular application remains an art. It is thus intriguing to answer a critical question: Given a task, how can we build a team of LLM agents to solve it effectively? Our new adaptive team-building paradigm offers a flexible solution, realized through a novel agent design named Captain Agent. It dynamically forms and manages teams for each step of a task-solving process, utilizing nested group conversations and reflection to ensure diverse expertise and prevent stereotypical outputs, allowing for a flexible yet structured approach to problem-solving. A comprehensive evaluation across six real-world scenarios demonstrates that Captain Agent significantly outperforms existing multi-agent methods with 21.94% improvement in average accuracy, providing outstanding performance without requiring task-specific prompt engineering. Our exploration of different backbone LLM and cost analysis further shows that Captain Agent can improve the conversation quality of weak LLM and achieve competitive performance with extremely low cost, which illuminates the application of multi-agent systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
