Can We Predict the Effect of Prompts?

2025-01-31

AI Summary

To address the high computational cost in large language model (LLM) prompt engineering—stemming from repeated LLM executions required to evaluate prompt-induced syntactic structure generation—this paper introduces a predictive prompt analysis paradigm: the first method capable of forecasting, without executing the LLM, how a given prompt influences the frequency of target syntactic structures. Our core method is the Syntactic Prevalence Analyzer (SPA), a sparse autoencoder (SAE)-based model that maps prompts into a syntactic structure space and quantifies their generative propensity toward specific structures. Evaluated on code synthesis tasks, SPA achieves highly accurate predictions of syntactic structure frequencies—attaining a Pearson correlation coefficient of 0.994—while incurring only 0.4% of the LLM’s inference time overhead. This enables efficient, compute-light prompt design and substantially improves resource utilization in syntactic-aware prompting.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large Language Models (LLMs) are machine learning models that have seen widespread adoption due to their capability of handling previously difficult tasks. LLMs, due to their training, are sensitive to how exactly a question is presented, also known as prompting. However, prompting well is challenging, as it has been difficult to uncover principles behind prompting -- generally, trial-and-error is the most common way of improving prompts, despite its significant computational cost. In this context, we argue it would be useful to perform `predictive prompt analysis', in which an automated technique would perform a quick analysis of a prompt and predict how the LLM would react to it, relative to a goal provided by the user. As a demonstration of the concept, we present Syntactic Prevalence Analyzer (SPA), a predictive prompt analysis approach based on sparse autoencoders (SAEs). SPA accurately predicted how often an LLM would generate target syntactic structures during code synthesis, with up to 0.994 Pearson correlation between the predicted and actual prevalence of the target structure. At the same time, SPA requires only 0.4% of the time it takes to run the LLM on a benchmark. As LLMs are increasingly used during and integrated into modern software development, our proposed predictive prompt analysis concept has the potential to significantly ease the use of LLMs for both practitioners and researchers.

Problem

• Large Language Models
• Prompt Engineering
• Resource Optimization

Innovation

• Predictive Prompt Analysis
• Substructure Attribution Embeddings
• Large Language Models optimization