吟游诗人：多智能体协作下的结构化提示生成，面向非人工智能专家

Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts

September 20, 2024

作者: Ming Wang, Yuanzhong Liu, Xiaoyu Liang, Yijie Huang, Daling Wang, Xiaocui Yang, Sijia Shen, Shi Feng, Xiaoming Zhang, Chaofeng Guan, Yifei Zhang

cs.AI

摘要

LLM在不同领域展现出了令人称赞的性能。然而，为协助它们工作而制定高质量提示对非人工智能专家来说是一项挑战。现有的提示工程研究表明，存在着一些分散的优化原则和依赖经验的提示优化器设计。不幸的是，这些努力缺乏结构设计，导致学习成本高，不利于提示的迭代更新，尤其是对非人工智能专家而言。受结构化可重用编程语言的启发，我们提出了LangGPT，一个结构化提示设计框架。此外，我们引入了Minstrel，一个具有反思能力的多生成代理系统，用于自动化生成结构化提示。实验证明，Minstrel生成的结构化提示或手动编写的提示显著提升了LLM的性能。此外，我们通过在线社区的用户调查分析了结构化提示的易用性。

English

LLMs have demonstrated commendable performance across diverse domains. Nevertheless, formulating high-quality prompts to assist them in their work poses a challenge for non-AI experts. Existing research in prompt engineering suggests somewhat scattered optimization principles and designs empirically dependent prompt optimizers. Unfortunately, these endeavors lack a structural design, incurring high learning costs and it is not conducive to the iterative updating of prompts, especially for non-AI experts. Inspired by structured reusable programming languages, we propose LangGPT, a structural prompt design framework. Furthermore, we introduce Minstrel, a multi-generative agent system with reflection to automate the generation of structural prompts. Experiments and the case study illustrate that structural prompts generated by Minstrel or written manually significantly enhance the performance of LLMs. Furthermore, we analyze the ease of use of structural prompts through a user survey in our online community.