Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts (2409.13449v1)

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.

• The paper presents a dual-layer LangGPT framework and a multi-agent Minstrel system that automate structural prompt generation for non-AI experts.
• The methodology leverages Analysis, Design, and Test groups to collaboratively design, evaluate, and refine prompts, yielding measurable improvements in LLM task performance.
• User feedback and experimental results demonstrate high satisfaction and notable performance gains, emphasizing the framework’s practical impact and reusability.

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

Overview

The paper, "Minstrel: Structural Prompt Generation with Multi-Agent Coordination for Non-AI Experts," presents a novel framework termed LangGPT for designing structural prompts. Additionally, it introduces Minstrel, a multi-agent system designed to generate these structural prompts automatically. The authors argue that the complexities and the empirical nature of current prompt-engineering practices make it difficult for non-AI experts to leverage LLMs effectively. LangGPT aims to provide a systematic, reusable, and flexible framework that simplifies this task by drawing inspiration from object-oriented programming languages.

LangGPT Framework

The LangGPT framework is based on a dual-layer structure with modules and elements. Modules are high-level components similar to classes in programming, while elements within these modules serve as specific instructions, akin to functions and properties. Key modules include:

• Role: Defines the role or identity the assistant should assume.
• Profile: Contains meta-information, aiding version management.
• Goal: Specifies the ultimate objectives of the task.
• Constraints: Lays out mandatory requirements or prohibitions.
• Examples: Provides input-output pairs for learning.
• Style: Describes stylistic guidelines for responses.

Elements within these modules can be concrete instructions (e.g., "The output should not exceed 500 words.") or more complex tasks that combine various instructions. The dual-layer structure aims to enhance the generalization and reusability of prompts, making them easier for non-specialists to master.

Minstrel: Automated Structural Prompt Generation

Minstrel leverages a multi-agent system to automate the generation of LangGPT prompts. It divides responsibilities among three working groups:

• Analysis Group (AG): Breaks down user-provided tasks and activates relevant module-design agents.
• Design Group (DG): Contains specialized agents for generating the content for each module.
• Test Group (TG): Simulates the task to evaluate the effectiveness of the generated prompts, using a combination of simulation and systematic testing.

The multi-agent system employs a collaborative design and reflection process to fine-tune the prompts. Reflection involves evaluating generated prompts, incorporating feedback, and iterating for improvement.

Experimental Results

The paper presents a wide range of quantitative results demonstrating the effectiveness of LangGPT and Minstrel. Six different LLMs are evaluated on tasks from diverse benchmarks such as GPQA, GSM8k, IFEval, and TruthfulQA. The evaluations reveal that LangGPT prompts significantly enhance the performance of LLMs across tasks, compared to baseline methods like COSTAR and CRISPE.

For instance:

• Instructed by LangGPT prompts, Qwen2-7B-Instruct achieves a 16.74 score on GPQA, surpassing the 10.94 score attained with CRISPE prompts.
• On the GSM8k task, Claude-3-haiku performs at 80.82 with LangGPT prompts, higher than the 78.47 achieved with COSTAR prompts.

Minstrel-generated prompts also perform competitively, occasionally surpassing manually designed ones.

Ease of Use and User Feedback

The authors report high user satisfaction and ease of use based on a comprehensive user survey within an online community. Results indicate that 89.66% of users rated the ease of use of LangGPT as 3 or higher on a 5-point scale. Additionally, the overall satisfaction with LangGPT averaged 8.55 out of 10.

Implications and Future Directions

The introduction of LangGPT and Minstrel holds several practical and theoretical implications:

• Accessibility: Simplifies prompt design for non-AI experts, democratizing the use of LLMs.
• Reusability: The structured framework ensures that prompts are reusable and adaptable to different tasks and models.
• Automation: The multi-agent system automates the otherwise labor-intensive prompt engineering.

Future research could focus on extending the LangGPT and Minstrel frameworks to be more adaptive to low-performing LLMs, addressing observed limitations in performance scaling. Additionally, further refinement of the collaboration and reflection mechanisms in Minstrel could enhance the quality of automatically generated prompts.

Conclusion

The paper provides a comprehensive framework and toolset for structural prompt generation, aimed at non-AI experts. The dual-layer LangGPT framework and the multi-agent Minstrel system together offer a robust solution for improving the efficiency and effectiveness of prompt engineering. Experimental results underscore the potential of these innovations to significantly enhance LLM performance, making advanced AI capabilities more accessible to a broader range of users.