Prompt Exploration with Prompt Regression

• URL: http://arxiv.org/abs/2405.11083v2
• Date: Mon, 26 Aug 2024 20:02:16 GMT
• Title: Prompt Exploration with Prompt Regression
• Authors: Michael Feffer, Ronald Xu, Yuekai Sun, Mikhail Yurochkin,
• Abstract summary: We propose a framework, Prompt Exploration with Prompt Regression (PEPR), to predict the effect of prompt combinations given results for individual prompt elements. We evaluate our approach with open-source LLMs of different sizes on several different tasks.
• Score: 38.847668543140315
• License: http://creativecommons.org/licenses/by/4.0/
• 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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