同时优化提示内容与格式，显著提升大模型表现
Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization
- 联合优化提示的内容与排版结构，迭代改进
- 在多个任务上比仅优化内容的方法提升明显
- 适用于各类开源大模型，无需针对特定模型调整
大语言模型（LLMs）在各类任务中展现出强大能力，其实际效果常由提示设计决定。尽管近期研究集中于优化提示内容，但提示格式这一关键却常被忽视的维度尚未得到系统性探索。本文提出内容-格式一体化提示优化（CFPO），通过迭代优化过程，联合优化提示内容与格式。CFPO利用自然语言变异探索内容变化，并采用动态格式探索策略，系统评估多种格式选项。在多个任务和开源LLM上的广泛实验表明，相比仅优化内容的方法，CFPO能带来可测量的性能提升。这凸显了内容与格式一体化优化的重要性，并提供了一种实用、模型无关的大模型性能增强方法。代码已公开于 https://github.com/HenryLau7/CFPO。
原文摘要 · Abstract (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 is available at https://github.com/HenryLau7/CFPO.
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