  Helping end users comprehend the abstract distribution shifts can greatly
facilitate AI deployment. Motivated by this, we propose a novel task, dataset
explanation. Given two image data sets, dataset explanation aims to
automatically point out their dataset-level distribution shifts with natural
language. Current techniques for monitoring distribution shifts provide
inadequate information to understand datasets with the goal of improving data
quality. Therefore, we introduce GSCLIP, a training-free framework to solve the
dataset explanation task. In GSCLIP, we propose the selector as the first
quantitative evaluation method to identify explanations that are proper to
summarize dataset shifts. Furthermore, we leverage this selector to demonstrate
the superiority of a generator based on language model generation. Systematic
evaluation on natural data shift verifies that GSCLIP, a combined system of a
hybrid generator group and an efficient selector is not only easy-to-use but
also powerful for dataset explanation at scale.
