Overview
This research investigates the automatic generation of task-specific prompt guidelines for Large Language Models (LLMs) to enhance their ability to accurately respond to user queries. The approach, termed AGOPS, aims to mitigate the issue of underspecified user prompts by evolving guidelines that help users formulate better-defined prompts. The study demonstrates AGOPS's effectiveness across mathematical reasoning, medical question answering, and coding tasks, showing substantial performance recovery compared to underspecified prompts.
Research Context
Reliable LLM responses depend on users clearly specifying requirements, context, and constraints within their queries. However, user queries are often underspecified, necessitating LLMs to infer assumptions that may not align with the user's actual intent. While existing prompt engineering guidelines exist, they are typically generic, task-agnostic, and formulated manually in a non-systematic manner, thus limiting their practical utility.
The problem addressed by this research is the optimization of prompt guidelines: the automatic generation of guidelines tailored to a specific task and model to facilitate the writing of more precisely specified prompts. A key observation underpinning this work is that existing, completed task examples (referred to as reference answers) often implicitly contain the missing information required to address underspecified queries. This includes behavioral constraints, contextual assumptions, and evaluation criteria.
Approach
The proposed approach, AGOPS (Automatic Guideline Optimization via Prompt Evolution and Self-Correction), evolves task-specific guidelines through an optimization scheme. This scheme involves three main components:
- Prompt LLM writer: This component is responsible for generating initial or refined prompt guidelines.
- Solver LLM: This LLM attempts to solve tasks based on prompts written using the generated guidelines.
- Prompt evolution: This mechanism iteratively refines the prompt guidelines. The evolution process maximizes downstream effectiveness on a dataset comprising user queries and their corresponding reference answers. The goal of this optimization is to generate guidelines that, when followed, lead to prompts that enable the solver LLM to achieve higher performance.
At inference time, the guidelines produced by AGOPS assist users in formulating well-specified prompts, which in turn boosts the effectiveness of LLMs.
Findings
- Prompt underspecification results in significant performance degradation for LLMs. Specifically, downstream task performance experienced drops of up to 95.3% when prompts were underspecified, compared to those that were well-specified.
- Existing prompt optimization techniques were largely ineffective at recovering the performance loss caused by underspecified prompts.
- Users who followed guidelines generated by AGOPS were able to regain a substantial portion of this performance loss. Performance increases ranged between 15.5% and 81.7% on average across all evaluated benchmarks.
- The consistent performance recovery was observed across diverse task domains, including mathematical reasoning, medical question answering, and coding tasks.
Why This Matters
The findings indicate that automatic generation of task-specific prompt guidelines can significantly improve the reliability and effectiveness of LLMs by addressing the pervasive issue of underspecified user queries. This approach offers a systematic method to mitigate performance degradation caused by ambiguous or incomplete prompts, leading to more accurate LLM outputs in practical applications.