Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization
Yuanye Liu, Jiahang Xu, Li Lyna Zhang, Qi Chen, Xuan Feng, Yang Chen, Zhongxin Guo, Yuqing Yang, Cheng Peng
2025-02-07
Summary
This paper talks about Content-Format Integrated Prompt Optimization (CFPO), a method that improves how AI models understand and respond to tasks by optimizing both the content and formatting of prompts.
What's the problem?
While large language models are powerful, their performance often depends on how prompts are designed. Most research focuses only on the content of prompts, ignoring how formatting (like structure and layout) can also impact performance. This leaves a gap in creating the most effective prompts.
What's the solution?
The researchers developed CFPO, which combines optimizing the content of prompts with optimizing their formatting. It uses an iterative process to test and improve different content variations and formatting styles. This method helps the AI better understand tasks by aligning both what is said (content) and how it is presented (formatting). They tested CFPO on multiple tasks and found it consistently improved performance compared to focusing on content alone.
Why it matters?
This research matters because it shows that formatting is just as important as content when designing prompts for AI models. By improving both aspects, CFPO makes AI systems more accurate and reliable across various tasks. This could lead to better performance in applications like customer support, education, and creative writing, making AI tools more effective overall.
Abstract
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.