PromptPrism: A Linguistically-Inspired Taxonomy for Prompts
Abstract: Prompts are the interface for eliciting the capabilities of 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.
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Glossary
- ANOVA: A statistical test used to compare means across groups to see if differences are significant. "ANOVA, p < 0.05"
- Chain-of-Thought (CoT): A prompting technique that asks models to show intermediate reasoning steps to improve performance. "Chain-of-Thought (CoT)"
- Component Organization: A schema for structuring prompt elements, including their positions and spans. "Component Organization: A component organization system plays on two primary mechanisms."
- Component Position: The indexing of each prompt element by role and sequence to locate it within the prompt. "Component Position assigns unique identifiers (Role, Index) to each prompt element"
- Component Span Analysis: The identification of start and end positions of each component to precisely define its boundaries. "Component Span Analysis defines exact boundaries through position markers (start_pos,end_pos)"
- Delimiter: A boundary marker separating components within a prompt. "We define the delimiter as the boundary between components."
- Delimiter Modification: An operation that alters the separators (e.g., newlines, tabs) used between prompt components. "delimiter modification operations"
- Directive Markers: Structural tokens or patterns (e.g., prefixes, suffixes) that signal instructions or boundaries in prompts. "Directive Markers and Patterns"
- Discourse Units: Coherent segments of text treated as functional parts of a larger communication structure. "organized discourse units"
- Few-shot Learning: A setting where a model is given a small number of input–output examples within the prompt to guide behavior. "few-shot learning"
- Function-Calling: A prompting scenario where the model is guided to invoke external tools or APIs with structured parameters. "function-calling scenarios"
- In-Context Learning: The ability of models to learn task behavior from examples provided directly in the prompt without parameter updates. "in-context learning"
- Linguistic Morphology: The study of the structure of words and morphemes, adapted here to analyze prompt-level markers. "linguistic morphology"
- Linguistic Pragmatics: The study of meaning in context, used to frame how prompts convey intentions and discourse purposes. "linguistic pragmatics"
- Linguistic Register: The stylistic level or formality of language expected in outputs, analogous to genre conventions. "linguistic register"
- Modality Space: The set of possible input content types (e.g., text, image, audio) and their combinations. "modality space "
- Multi-modal Prompts: Prompts that combine multiple input modalities such as text, images, or audio. "multi-modal prompts"
- Plan-based Intentions: The idea that prompts are crafted with goal-directed plans that structure their discourse. "plan-based intentions"
- Pragmatic Implicatures: Implied meanings or effects beyond literal content that can influence interpretation. "pragmatic implicatures"
- Rouge-L: An automatic evaluation metric based on longest common subsequence overlap between generated and reference text. "Rouge-L metrics"
- Satisfaction-Precedence Relations: Hierarchical semantic relations where lower-level components contribute to fulfilling higher-level purposes in order. "satisfaction-precedence relations"
- Semantic Operators: Controlled manipulations at the meaning level (e.g., permute, add, delete components) used to test sensitivity. "semantic operators (permute, add, delete)"
- Syntactic Operators: Controlled manipulations of form and formatting (e.g., delimiter or layout changes) used to test sensitivity. "syntactic operators (format and delimiter modifications)"
- System Prompt: A special role/section in the prompt that sets global instructions and behavior for the assistant. "System Prompt"
- Taxonomic Tree Width: A measure of semantic component breadth in a prompt’s hierarchical decomposition. "mean taxonomic tree width"
- Taxonomy-Guided Prompt Refinement: A method that rewrites prompts using the taxonomy’s structural and semantic insights to improve performance. "taxonomy-guided prompt refinement"
- Zero-shot Settings: Scenarios where the model receives no examples in the prompt and must perform the task directly from instructions. "zero-shot settings"
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