人机协作优化提示词，让非专家也能高效定制AI指令
iPrOp: Interactive Prompt Optimization for Large Language Models with a Human in the Loop
- 引入交互式提示优化框架，支持用户实时评估与调整提示
- 通过模型预测与解释增强用户对优化过程的理解与控制
- 适合领域专家参与，也可用于研究提示性能影响因素
提示工程在大语言模型时代发挥了重要作用，但其效果依赖于提示设计者的技能。本文提出iPrOp，一种新型人机交互式提示优化方法，结合手动与自动优化优势，让用户可评估不断演化的提示。该方法通过提示变体、信息性示例、大模型生成的预测及其解释、相关性能指标等结构化信息，为用户提供任务特定指导，提升用户参与度。用户可根据自身偏好和需求选择并进一步优化提示。该方法不仅帮助非技术领域专家生成适配特定任务或领域的最优提示，还可用于研究影响提示优化性能的内在参数。评估表明，该方法能生成改进后的提示，从而提升任务表现。
原文摘要 · Abstract (English)
Prompt engineering has made significant contributions to the era of large language models, yet its effectiveness depends on the skills of a prompt author. This paper introduces $\textit{iPrOp}$, a novel interactive prompt optimization approach, to bridge manual prompt engineering and automatic prompt optimization while offering users the flexibility to assess evolving prompts. We aim to provide users with task-specific guidance to enhance human engagement in the optimization process, which is structured through prompt variations, informative instances, predictions generated by large language models along with their corresponding explanations, and relevant performance metrics. This approach empowers users to choose and further refine the prompts based on their individual preferences and needs. It can not only assist non-technical domain experts in generating optimal prompts tailored to their specific tasks or domains, but also enable to study the intrinsic parameters that influence the performance of prompt optimization. The evaluation shows that our approach has the capability to generate improved prompts, leading to enhanced task performance.
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