Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization (2502.04295v3)

Abstract: 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.

Insights on Content-Format Integrated Prompt Optimization

The paper "Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization" presents a novel framework to enhance the performance of LLMs by integrating both the content and format of prompts into the optimization process. Unlike previous research which predominantly focused on optimizing the textual content of prompts, this paper highlights the underserved yet critical dimension of prompt formatting.

Key Methodological Contributions

The primary contribution of this paper is the introduction of Content-Format Integrated Prompt Optimization (CFPO), which optimizes prompt content and format simultaneously. This methodology is rooted in a structured prompt template that differentiates between content-based components (such as task instructions and few-shot examples) and format-based components (like query format and prompt renderer).

CFPO employs an iterative optimization approach supported by two optimizers: a Component-wise Content Optimizer and a Format Optimizer. The former focuses on improving the textual content using feedback-driven mutations and Monte Carlo sampling, while the latter explores various formatting options through an LLM-assisted format generation strategy and a dynamic format exploration mechanism utilizing Upper Confidence Bounds applied to Trees (UCT).

Significant Findings and Results

The paper reports that CFPO delivers measurable improvements in LLM performance across several tasks and models, surpassing traditional content-only optimization techniques. Evaluation results on datasets such as GSM8K and MATH500, among others, demonstrate significant gains. For instance, on the GSM8K dataset, the CFPO method enhanced performance to 53.22% using Mistral-7B-v0.1, compared to 45.72% using the ProTeGi method, which underscores the efficacy of integrating prompt format into the optimization process.

The findings indicate that different LLMs possess unique formatting preferences, and no singular format consistently maximizes performance across all contexts, accentuating the necessity for a flexible, integrated optimization framework. Notably, instruction-tuned models exhibit more robust results, presumably due to their inherent alignment with diverse task-specific contexts during training.

Implications and Future Directions

The implications of CFPO are substantial, impacting both practical applications and theoretical advancements in AI. Practically, this approach offers a model-agnostic pathway that could be employed to boost the operational efficiency of LLMs in various applications, from natural language understanding to task-oriented dialogue systems. Theoretically, the research opens new avenues in understanding the intricate interplay between content and format in prompt engineering, fostering advancements in the metaknowledge of LLMs.

Future research may explore automated strategies for prompt optimization, possibly leveraging reinforcement learning techniques to further refine both content and format in real time. Moreover, expanding the framework to include multimodal data may unveil additional layers of complexity and optimization potential that extend beyond textual prompts.

In essence, this paper establishes CFPO as a critical step towards enhancing LLM capabilities through a comprehensive understanding of prompt design, empowering users to harness the full potential of artificial intelligence in diverse applications.