Prompt Exploration with Prompt Regression
<|reference_start|>Prompt Exploration with Prompt Regression: 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.<|reference_end|>
@article{feffer2024prompt,
    title={Prompt Exploration with Prompt Regression},
    author={Michael Feffer, Ronald Xu, Yuekai Sun, Mikhail Yurochkin},
    journal={arXiv preprint arXiv:2405.11083},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11083},
    primaryClass={cs.CL cs.LG}
}