用反思式进化优化语言模型提示词，效果优于现有方法。
ReflectivePrompt: Reflective evolution in autoprompting algorithms
- 通过短期与长期反思机制改进进化算法中的交叉和变异操作
- 在33个数据集上平均提升28%（如BBH任务），超越当前最优方法
- 适合关注提示工程与进化算法结合的研究者或应用开发者
自动提示生成是利用进化算法自动寻找语言模型最优提示的热门方向。本文提出ReflectivePrompt，一种基于反思演化的新型自动提示方法，通过在交叉前引入短时反思、在突变中加入长时反思，实现对进化过程知识的积累与动态更新。该方法在33个分类与文本生成数据集上，使用t-lite-instruct-0.1和gemma3-27b-it两个开源大模型进行测试，结果显示其在指标上平均显著优于现有先进方法，例如在BBH任务上相比EvoPrompt提升28%，展现出卓越的搜索能力，确立了其在基于进化算法的自动提示领域中的领先地位。
原文摘要 · Abstract (English)
Autoprompting is the process of automatically selecting optimized prompts for language models, which has been gaining popularity with the rapid advancement of prompt engineering, driven by extensive research in the field of large language models (LLMs). This paper presents ReflectivePrompt - a novel autoprompting method based on evolutionary algorithms that employs a reflective evolution approach for more precise and comprehensive search of optimal prompts. ReflectivePrompt utilizes short-term and long-term reflection operations before crossover and elitist mutation to enhance the quality of the modifications they introduce. This method allows for the accumulation of knowledge obtained throughout the evolution process and updates it at each epoch based on the current population. ReflectivePrompt was tested on 33 datasets for classification and text generation tasks using open-access large language models: t-lite-instruct-0.1 and gemma3-27b-it. The method demonstrates, on average, a significant improvement (e.g., 28% on BBH compared to EvoPrompt) in metrics relative to current state-of-the-art approaches, thereby establishing itself as one of the most effective solutions in evolutionary algorithm-based autoprompting.
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