mlsanityData to 7 Oct 2026

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

arXiv 2502.04295 ↗First posted 6 Feb 2025cs.CLcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 29 of its 66 references that are in the map. First posted on arXiv 6 Feb 2025.

Builds on

29 of 66 references.

References · 29

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  4. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  5. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  6. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  7. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  8. 2023Let's Verify Step by Stepcited by 3,598
  9. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,883
  10. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
  11. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 803
  12. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 778
17 more
  1. 2023Automatic Prompt Optimization with "Gradient Descent" and Beam Searchcited by 628
  2. 2023Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolutioncited by 521
  3. 2023State of What Art? A Call for Multi-Prompt LLM Evaluationcited by 299
  4. 2023Large Language Models as Tool Makerscited by 293
  5. 2023PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimizationcited by 226
  6. 2024ProSA: Assessing and Understanding the Prompt Sensitivity of LLMscited by 145
  7. 2024Does Prompt Formatting Have Any Impact on LLM Performance?cited by 132
  8. 2023Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generationcited by 128
  9. 2023Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learningcited by 115
  10. 2024The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performancecited by 97
  11. 2024Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvementscited by 82
  12. 2022Language Models Can Teach Themselves to Program Bettercited by 75
  13. 2024POSIX: A Prompt Sensitivity Index For Large Language Modelscited by 56
  14. 2024MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQLcited by 34
  15. 2024Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimizationcited by 29
  16. 2024LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Languagecited by 12
  17. 2024Q-Tuning: Queue-based Prompt Tuning for Lifelong Few-shot Language Learningcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 7.

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  1. 2026SkCC: Portable and Secure Skill Compilation for Cross-Framework LLM Agentscited by 7
  2. 2025Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategiescited by 2
  3. 2025Bounds of Chain-of-Thought Robustness: Reasoning Steps, Embed Norms, and Beyondcited by 2
  4. 2025Prompt and Parameter Co-Optimization for Large Language Modelscited by 2
  5. 2025A Survey of LLM Prompt Datasets: Taxonomy, Linguistic Patterns, and Practical Usescited by 2
  6. 2025Prompt Orchestration Markup Languagecited by 1
  7. 2025Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Levelcited by 1
  8. 2026FMBench: Adaptive Large Language Model Output Formattingcited by 1
  9. 2026How Confident Is the First Token? An Uncertainty-Calibrated Prompt Optimization Framework for Large Language Model Classification and Understandingcited by 1
  10. 2026View-oriented Conversation Compiler for Agent Trace Analysiscited by 1
  11. 2026Optimization before Evaluation: Evaluation with Unoptimised Prompts Can be Misleadingcited by 1
  12. 2025Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmapcited by 0
3 more
  1. 2025Reasoning with Preference Constraints: A Benchmark for Language Models in Many-to-One Matching Marketscited by 0
  2. 2026OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithmscited by 0
  3. 2026GTA: Graph Theory Agent and Benchmark for Algorithmic Graph Reasoning with LLMscited by 0