Abstract
Autoprompting is the process of automatically selecting optimized prompts for language models, which is gaining popularity due to the rapid development of prompt engineering driven by extensive research in the field of large language models (LLMs). This paper presents DistillPrompt -- a novel autoprompting method based on large language models that employs a multi-stage integration of task-specific information into prompts using training data. DistillPrompt utilizes distillation, compression, and aggregation operations to explore the prompt space more thoroughly. The method was tested on different datasets for text classification and generation tasks using the t-lite-instruct-0.1 language model. The results demonstrate a significant average improvement (e.g., 20.12% across the entire dataset compared to Grips) in key metrics over existing methods in the field, establishing DistillPrompt as one of the most effective non-gradient approaches in autoprompting.
Abstract (translated)
自动生成提示词（Autoprompting）是指自动选择优化后的提示词供语言模型使用，这一方法由于在大规模语言模型（LLMs）领域中广泛研究而推动的提示工程快速发展变得越来越受欢迎。本文介绍了一种基于大型语言模型的新颖自动生成提示词方法——DistillPrompt。该方法通过多阶段整合任务特定信息到提示词中，并利用训练数据进行操作，包括蒸馏、压缩和聚合等步骤来更彻底地探索提示空间。 在文本分类和生成任务的不同数据集上使用t-lite-instruct-0.1语言模型对DistillPrompt进行了测试。实验结果显示，在关键性能指标方面，与现有方法相比（例如，相较于Grips在整个数据集上的平均改进达20.12%），DistillPrompt表现出显著的平均改善效果，并确立了其作为非梯度自动生成提示词方法中最有效的技术之一的地位。
URL
https://arxiv.org/abs/2508.18992
