PAS：数据高效即插即用提示增强系统

摘要

近年来，大型语言模型（LLMs）的兴起推动了对即插即用人工智能系统的增长需求。在各种人工智能技术中，提示工程显得尤为重要。然而，由于陡峭的学习曲线和大量时间投入，用户经常在撰写提示时面临挑战，而现有的自动提示工程（APE）模型可能难以使用。为解决这一问题，我们提出了PAS，一种基于LLM的即插即用APE系统。PAS利用在高质量自动生成的提示互补数据集上训练的LLMs，表现出卓越的性能。在全面的基准测试中，PAS相比以往的APE模型取得了最先进的结果，平均改进了6.09个点。此外，PAS高效率，仅需9000个数据点即可达到最先进的性能。此外，PAS可以自动生成提示增强数据，无需额外的人力。其灵活性也使其与所有现有的LLMs兼容，并适用于各种任务。PAS在人类评估中表现出色，突显了其作为用户插件的适用性。PAS在高性能、高效率和灵活性的结合下，成为通过改进提示工程来增强LLMs的可用性和效果的宝贵系统。

English

In recent years, the rise of Large Language Models (LLMs) has spurred a growing demand for plug-and-play AI systems. Among the various AI techniques, prompt engineering stands out as particularly significant. However, users often face challenges in writing prompts due to the steep learning curve and significant time investment, and existing automatic prompt engineering (APE) models can be difficult to use. To address this issue, we propose PAS, an LLM-based plug-and-play APE system. PAS utilizes LLMs trained on high-quality, automatically generated prompt complementary datasets, resulting in exceptional performance. In comprehensive benchmarks, PAS achieves state-of-the-art (SoTA) results compared to previous APE models, with an average improvement of 6.09 points. Moreover, PAS is highly efficient, achieving SoTA performance with only 9000 data points. Additionally, PAS can autonomously generate prompt augmentation data without requiring additional human labor. Its flexibility also allows it to be compatible with all existing LLMs and applicable to a wide range of tasks. PAS excels in human evaluations, underscoring its suitability as a plug-in for users. This combination of high performance, efficiency, and flexibility makes PAS a valuable system for enhancing the usability and effectiveness of LLMs through improved prompt engineering.

PAS：数据高效即插即用提示增强系统

PAS: Data-Efficient Plug-and-Play Prompt Augmentation System

摘要

Summary

Support

Support