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Computer Science > Computation and Language

arXiv:2409.13449 (cs)
[Submitted on 20 Sep 2024]

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

Authors:Ming Wang, Yuanzhong Liu, Xiaoyu Liang, Yijie Huang, Daling Wang, Xiaocui Yang, Sijia Shen, Shi Feng, Xiaoming Zhang, Chaofeng Guan, Yifei Zhang
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Abstract: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.
Comments: arXiv admin note: text overlap with arXiv:2402.16929
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2409.13449 [cs.CL]
  (or arXiv:2409.13449v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2409.13449

Submission history

From: Ming Wang [view email]
[v1] Fri, 20 Sep 2024 12:30:03 UTC (6,167 KB)
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