In late 2020, the grassroots research collective EleutherAI published The Pile, a large English-language training corpus designed to be openly documented rather than secret. The accompanying paper, “The Pile: An 825 GiB Dataset of Diverse Text for Language Modeling” by Leo Gao, Stella Biderman, Sid Black, and colleagues, was posted to arXiv on December 31, 2020. The project page summarizes it plainly: “The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together.”
What made The Pile notable was not just its size but its transparency. Many large models of the era were trained on undisclosed mixtures of web text. The Pile, by contrast, named all 22 of its component datasets, drawing on sources such as web crawl data, books, code, academic papers, and other curated collections. The paper states the dataset is “constructed from 22 diverse high-quality subsets — both existing and newly constructed,” so anyone could see, and critique, exactly what went into the corpus.
The Pile also made an empirical argument about data quality over raw quantity. The authors report that “models trained on the Pile improve significantly over both Raw CC and CC-100 on all components of the Pile,” where CC refers to Common Crawl. In other words, a thoughtfully assembled mixture of diverse sources outperformed models trained on undifferentiated web crawl alone, evidence that how you assemble training data matters, not just how much you have.
Why business readers should care: The Pile is one of the clearest examples of what actually goes into a language model. For organizations evaluating AI, it is a useful reference point for two reasons: it shows that training data is a deliberate, documentable mixture of identifiable sources, and it demonstrates that careful data curation is itself a lever on model quality.