title: Beyond Prompt Content: Enhancing LLM Performance via Content-Format
    Integrated Prompt Optimization
summary: 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.
paper_authors._id: 67a57d32bc587f5b57a3f24f
paper_authors.name: Yuanye Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T08:26:09.117Z
paper_authors._id: 66825791c238f05e95f53f60
paper_authors.avatarUrl: /avatars/db771a39095ec5e9125959d1d919d593.svg
paper_authors.fullname: Yuanye Liu
paper_authors.type: user
paper_authors.user: HenryLau7
paper_authors._id: 67a57d32bc587f5b57a3f250
paper_authors.name: Jiahang Xu
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-02-07T03:25:39.760Z
paper_authors._id: 62abdf657b037eafffc48808
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1655430982462-noauth.jpeg
paper_authors.fullname: Jiahang Xu
paper_authors.type: user
paper_authors.user: Jiahang
paper_authors._id: 67a57d32bc587f5b57a3f251
paper_authors.name: Li Lyna Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-07T10:47:55.350Z
paper_authors._id: 62b0009c72043b05d29492b2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62b0009c72043b05d29492b2/NqRkX2YLhlfOLvYysa7dD.png
paper_authors.fullname: Li Lyna Zhang
paper_authors.type: user
paper_authors.user: lynazhang
paper_authors._id: 67a57d32bc587f5b57a3f252
paper_authors.name: Qi Chen
paper_authors._id: 67a57d32bc587f5b57a3f253
paper_authors.name: Xuan Feng
paper_authors._id: 67a57d32bc587f5b57a3f254
paper_authors.name: Yang Chen
paper_authors._id: 67a57d32bc587f5b57a3f255
paper_authors.name: Zhongxin Guo
paper_authors._id: 67a57d32bc587f5b57a3f256
paper_authors.name: Yuqing Yang
paper_authors._id: 67a57d32bc587f5b57a3f257
paper_authors.name: Cheng Peng
paper_title: Beyond Prompt Content: Enhancing LLM Performance via Content-Format
    Integrated Prompt Optimization
paper_summary: 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.
paper_ai_summary: CFPO, an integrated optimization method for both content and formatting of prompts, enhances LLM performance across tasks.
paper_ai_keywords: Large Language Models prompt optimization content-format integrated optimization natural language mutations dynamic format exploration