Predictive Prompt Analysis

TL;DR

This paper introduces a predictive prompt analysis method called SPA that estimates LLM responses to prompts efficiently, aiding prompt design without extensive trial-and-error.

Contribution

It presents SPA, a novel autoencoder-based approach for predicting LLM behavior, significantly reducing computational costs in prompt analysis.

Findings

SPA achieved up to 0.994 Pearson correlation with actual LLM responses.

SPA required only 0.4% of the time needed for running the LLM.

Demonstrated effectiveness in predicting syntactic structures during code synthesis.

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.