In the advent of democratized usage of large language models (LLMs), there is a growing desire to systematize LLM prompt creation and selection processes beyond iterative trial-and-error. Prior works majorly focus on searching the space of prompts without accounting for relations between prompt variations. Here we propose a framework, Prompt Exploration with Prompt Regression (PEPR), to predict the effect of prompt combinations given results for individual prompt elements as well as a simple method to select an effective prompt for a given use-case. We evaluate our approach with open-source LLMs of different sizes on several different tasks.
@article{arxiv.2405.11083,
title = {Prompt Exploration with Prompt Regression},
author = {Michael Feffer and Ronald Xu and Yuekai Sun and Mikhail Yurochkin},
journal= {arXiv preprint arXiv:2405.11083},
year = {2024}
}