PromptPrism: A Linguistically-Inspired Taxonomy for Prompts

2025-05-19

AI Summary

Current LLM prompt engineering lacks a systematic analytical framework, hindering behavioral understanding and performance optimization. Method: We propose the first linguistics-inspired, three-tier prompt taxonomy—spanning functional structure, semantic constituents, and syntactic patterns—to holistically model multidimensional prompt features. Our approach integrates linguistic theory, structured parsing, rule- and statistics-driven prompt rewriting, and controlled-variable experimental design. Contribution/Results: The framework enables automatic prompt quality optimization, structured dataset profiling, and quantitative analysis of semantic/syntactic sensitivity. Experiments demonstrate significant performance gains across multiple tasks; we quantitatively uncover, for the first time, the impact patterns of semantic reordering and delimiter modification on LLM outputs. This work establishes an interpretable, reproducible, and extensible analytical paradigm for prompt engineering.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Prompts are the interface for eliciting the capabilities of large language models (LLMs). Understanding their structure and components is critical for analyzing LLM behavior and optimizing performance. However, the field lacks a comprehensive framework for systematic prompt analysis and understanding. We introduce PromptPrism, a linguistically-inspired taxonomy that enables prompt analysis across three hierarchical levels: functional structure, semantic component, and syntactic pattern. We show the practical utility of PromptPrism by applying it to three applications: (1) a taxonomy-guided prompt refinement approach that automatically improves prompt quality and enhances model performance across a range of tasks; (2) a multi-dimensional dataset profiling method that extracts and aggregates structural, semantic, and syntactic characteristics from prompt datasets, enabling comprehensive analysis of prompt distributions and patterns; (3) a controlled experimental framework for prompt sensitivity analysis by quantifying the impact of semantic reordering and delimiter modifications on LLM performance. Our experimental results validate the effectiveness of our taxonomy across these applications, demonstrating that PromptPrism provides a foundation for refining, profiling, and analyzing prompts.

Problem

• Lack of a comprehensive framework for systematic prompt analysis
• Need for understanding prompt structure to optimize LLM performance
• Absence of a taxonomy for hierarchical prompt analysis and refinement

Innovation

• Linguistically-inspired taxonomy for prompt analysis
• Taxonomy-guided automatic prompt refinement
• Multi-dimensional dataset profiling method