Beyond Prompt Content: Enhancing LLM Performance via Content-Format
  Integrated Prompt Optimization
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.
67a57d32bc587f5b57a3f24f
Yuanye Liu
claimed_verified
2025-02-13T08:26:09.117Z
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Yuanye Liu
user
HenryLau7
67a57d32bc587f5b57a3f250
Jiahang Xu
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2025-08-20T03:30:09.524Z
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Jiahang Xu
user
Jiahang
67a57d32bc587f5b57a3f251
Li Lyna Zhang
admin_assigned
2025-02-07T10:47:55.350Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/62b0009c72043b05d29492b2/NqRkX2YLhlfOLvYysa7dD.png
Li Lyna Zhang
user
lynazhang
67a57d32bc587f5b57a3f252
Qi Chen
67a57d32bc587f5b57a3f253
Xuan Feng
67a57d32bc587f5b57a3f254
Yang Chen
67a57d32bc587f5b57a3f255
Zhongxin Guo
67a57d32bc587f5b57a3f256
Yuqing Yang
67a57d32bc587f5b57a3f257
Cheng Peng
Beyond Prompt Content: Enhancing LLM Performance via Content-Format
  Integrated Prompt Optimization
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.
CFPO, an integrated optimization method for both content and formatting of prompts, enhances LLM performance across tasks.
Large Language Models prompt optimization content-format integrated optimization natural language mutations dynamic format exploration