超越提示内容：通过内容格式一体化提示优化提升LLM性能

Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization

February 6, 2025

作者: Yuanye Liu, Jiahang Xu, Li Lyna Zhang, Qi Chen, Xuan Feng, Yang Chen, Zhongxin Guo, Yuqing Yang, Cheng Peng

cs.AI

摘要

大型语言模型（LLMs）在各种任务中展现出显著的能力，其真实世界的有效性通常受到提示设计的驱动。虽然最近的研究集中在优化提示内容上，但提示格式的作用，作为一个关键但经常被忽视的维度，却受到了有限的系统性调查。在本文中，我们介绍了内容-格式一体化提示优化（CFPO），这是一种创新方法，通过迭代的优化过程共同优化提示内容和格式。CFPO利用自然语言变异来探索内容变化，并采用动态格式探索策略，系统评估各种格式选项。我们在多个任务和开源LLMs上进行了广泛评估，结果显示CFPO相较于仅优化内容的方法表现出可衡量的性能改进。这突显了整合内容-格式优化的重要性，并提供了一个实用的、与模型无关的方法来增强LLM的性能。代码将在https://github.com/HenryLau7/CFPO 上提供。

English

Large Language Models (LLMs) have shown significant capability across various tasks, with their real-world effectiveness often driven by prompt design. While recent research has focused on optimizing prompt content, the role of prompt formatting, a critical but often overlooked dimension, has received limited systematic investigation. In this paper, we introduce Content-Format Integrated Prompt Optimization (CFPO), an innovative methodology that jointly optimizes both prompt content and formatting through an iterative refinement process. CFPO leverages natural language mutations to explore content variations and employs a dynamic format exploration strategy that systematically evaluates diverse format options. Our extensive evaluations across multiple tasks and open-source LLMs demonstrate that CFPO demonstrates measurable performance improvements compared to content-only optimization methods. This highlights the importance of integrated content-format optimization and offers a practical, model-agnostic approach to enhancing LLM performance. Code will be available at https://github.com/HenryLau7/CFPO.