Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts
Project Overview
The document explores the integration of generative AI in education through the introduction of LangGPT, a framework designed to improve the usability of Large Language Models (LLMs) for non-AI experts. It highlights the crucial role of prompt engineering in optimizing LLM interactions and presents Minstrel, a multi-agent system that automates the generation of structural prompts. This innovation addresses common challenges faced by non-experts, such as difficulties in effectively designing prompts, thereby enhancing LLM performance and user satisfaction. By simplifying the prompt design process, LangGPT and Minstrel empower educators and students alike to leverage AI tools more effectively, ultimately leading to improved educational outcomes. The findings indicate that with the right support, generative AI can significantly enhance learning experiences by making advanced technologies more accessible and user-friendly for a broader audience.
Key Applications
Minstrel: Structural Prompt Generation with Multi-Agents
Context: Designed for non-AI experts to enhance their ability to use LLMs effectively across various tasks.
Implementation: Minstrel employs a multi-agent system that includes an analysis group, design group, and test group to collaborate on prompt generation.
Outcomes: Significantly enhanced performance of LLMs, easier prompt design for non-experts, and higher user satisfaction.
Challenges: Initial complexity and learning curve for non-experts, adaptability issues for low-performing LLMs.
Implementation Barriers
Usability Barrier
Non-AI experts find it challenging to formulate effective prompts for LLMs, which can be complex and require technical skills. The development of LangGPT and Minstrel aims to simplify the prompt design process and reduce learning costs.
Performance Barrier
Structural prompts are poorly adapted to low-performance LLMs, limiting their effectiveness in certain contexts. Future work will focus on optimizing prompt design specifically for low-performance models.
Project Team
Ming Wang
Researcher
Yuanzhong Liu
Researcher
Xiaoyu Liang
Researcher
Yijie Huang
Researcher
Daling Wang
Researcher
Xiaocui Yang
Researcher
Sijia Shen
Researcher
Shi Feng
Researcher
Xiaoming Zhang
Researcher
Chaofeng Guan
Researcher
Yifei Zhang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Ming Wang, Yuanzhong Liu, Xiaoyu Liang, Yijie Huang, Daling Wang, Xiaocui Yang, Sijia Shen, Shi Feng, Xiaoming Zhang, Chaofeng Guan, Yifei Zhang
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai