吟遊詩人：結構提示生成與多智能體協調，針對非人工智慧專家

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在各個領域展現了令人讚賞的表現。然而，為了協助它們的工作而制定高質量提示對非人工智慧專家來說是一項挑戰。現有的提示工程研究表明，優化原則和設計有些零散，並且依賴實證的提示優化器。不幸的是，這些努力缺乏結構設計，導致高學習成本，並且不利於提示的迭代更新，尤其是對非人工智慧專家而言。受結構化可重複使用的編程語言的啟發，我們提出了LangGPT，一個結構提示設計框架。此外，我們引入了Minstrel，一個具有反思能力的多生成代理系統，用於自動生成結構提示。實驗和案例研究說明了Minstrel生成的結構提示或手動編寫的提示顯著提升了LLM的性能。此外，我們通過在線社區的用戶調查分析了結構提示的易用性。

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.