Prompt Exploration with Prompt Regression

Michael Feffer, Ronald Xu, Yuekai Sun, Mikhail Yurochkin

Research Area: Alignment, Science of LMs

Keywords: prompt engineering, prompt selection, large language models, regression

TL;DR: We introduce a regression method to predict effects of prompt combinations as well as select effective prompts based on the regression model accordingly.

Abstract: 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.

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Submission Number: 239