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Recent literature has underscored the importance of dataset documentationwork for machine learning, and part of this work involves addressing"documentation debt" for datasets that have been used widely but documentedsparsely. This paper aims to help address documentation debt for BookCorpus, apopular text dataset for training large language models. Notably, researchershave used BookCorpus to train OpenAI's GPT-N models and Google's BERT models,even though little to no documentation exists about the dataset's motivation,composition, collection process, etc. We offer a preliminary datasheet thatprovides key context and information about BookCorpus, highlighting severalnotable deficiencies. In particular, we find evidence that (1) BookCorpuslikely violates copyright restrictions for many books, (2) BookCorpus containsthousands of duplicated books, and (3) BookCorpus exhibits significant skews ingenre representation. We also find hints of other potential deficiencies thatcall for future research, including problematic content, potential skews inreligious representation, and lopsided author contributions. While more workremains, this initial effort to provide a datasheet for BookCorpus adds togrowing literature that urges more careful and systematic documentation formachine learning datasets.

Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus