  Large language models (LLMs) have displayed an impressive ability to harness
natural language to perform complex tasks. In this work, we explore whether we
can leverage this learned ability to find and explain patterns in data.
Specifically, given a pre-trained LLM and data examples, we introduce
interpretable autoprompting (iPrompt), an algorithm that generates a
natural-language string explaining the data. iPrompt iteratively alternates
between generating explanations with an LLM and reranking them based on their
performance when used as a prompt. Experiments on a wide range of datasets,
from synthetic mathematics to natural-language understanding, show that iPrompt
can yield meaningful insights by accurately finding groundtruth dataset
descriptions. Moreover, the prompts produced by iPrompt are simultaneously
human-interpretable and highly effective for generalization: on real-world
sentiment classification datasets, iPrompt produces prompts that match or even
improve upon human-written prompts for GPT-3. Finally, experiments with an fMRI
dataset show the potential for iPrompt to aid in scientific discovery. All code
for using the methods and data here is made available on Github.
