Abstract: How do two \textit{distributions} of text differ?Humans are slow at answering this, since discovering patterns might require tediously reading through hundreds of samples.We propose to automatically summarize the differences by ``learning a natural language hypothesis":given two distributions $D_{0}$ and $D_{1}$, we search for a description that is more often true for $D_{1}$, e.g., ``\textit{is military-related.}"To tackle this problem, we fine-tune GPT-3 to propose descriptions with the prompt: ``[samples of $D_{0}$] + [samples of $D_{1}$] + \textit{the difference between them is \underline{\space\space\space\space}}".We then re-rank the descriptions by checking how often they hold on a larger set of samples with a learned verifier.On a benchmark of 54 real-world binary classification tasks, while GPT-3 Curie (13B) only generates a description similar to human annotation 7\% of the time, the performance reaches 61\% with fine-tuning and re-ranking, and our best system using GPT-3 Davinci (175B) reaches 76\%.We apply our system to describe distribution shifts, debug dataset shortcuts, summarize unknown tasks, and label text clusters, and present analyses based on automatically generated descriptions.