The Pile: An 800GB Dataset of Diverse Text for Language Modeling (2101.00027v1)

Abstract: Recent work has demonstrated that increased training dataset diversity improves general cross-domain knowledge and downstream generalization capability for large-scale LLMs. With this in mind, we present \textit{the Pile}: an 825 GiB English text corpus targeted at training large-scale LLMs. The Pile is constructed from 22 diverse high-quality subsets -- both existing and newly constructed -- many of which derive from academic or professional sources. Our evaluation of the untuned performance of GPT-2 and GPT-3 on the Pile shows that these models struggle on many of its components, such as academic writing. Conversely, models trained on the Pile improve significantly over both Raw CC and CC-100 on all components of the Pile, while improving performance on downstream evaluations. Through an in-depth exploratory analysis, we document potentially concerning aspects of the data for prospective users. We make publicly available the code used in its construction.

The Pile: An 800GB Dataset of Diverse Text for LLMing

The paper "The Pile: An 800GB Dataset of Diverse Text for LLMing" introduces a comprehensive text corpus specifically designed to address the growing need for high-quality data in training large-scale LLMs. Authored by Leo Gao, Stella Biderman, Sid Black, Laurence Golding, and others from EleutherAI, the paper makes several significant contributions to the field of NLP.

Motivation and Construction

The primary motivation behind the Pile is the recent evidence suggesting that increased training dataset diversity enhances a LLM's generalization capabilities across various domains. With most current models heavily relying on datasets like Common Crawl, there is an undeniable need for a diversified text corpus. The Pile addresses this by integrating 22 distinct and high-quality subsets, derived from both existing datasets and newly constructed sources. These subsets include data from PubMed Central, ArXiv, GitHub, Stack Exchange, and Project Gutenberg, amongst others.

Composition and Quality

The Pile encompasses a total of 825 GiB of English text, meticulously curated to include high-quality selections from diverse domains such as academia, code repositories, legal documents, and dialogue data. Of particular note are the newly introduced datasets from sources like YouTube, PhilPapers, and NIH ExPorter, each contributing unique content to the corpus. The Pile transcends the limitations of traditional web-scraped datasets by incorporating specialized subsets that cover scientific literature, legal documents, and dialogue-rich environments like IRC chat logs.

Methodology

The dataset is processed to ensure minimal duplication using MinHashLSH techniques and is aggressively filtered for quality using a classifier trained on recognized high-quality data (OpenWebText2). The authors leveraged jusText and other tools for extracting meaningful content from raw HTML, ensuring a cleaner and more coherent dataset compared to the Common Crawl's raw outputs.

Evaluation

A rigorous evaluation of GPT-2 and GPT-3 models on the Pile reveals important insights. The performance of these untuned models on the Pile's components indicates significant challenges in academic writing and other specialized domains. Nevertheless, models trained on the Pile display marked improvements over those trained on Raw CC or CC-100 datasets.

Numerical Results

The assessment of GPT-2 and GPT-3 models trained on the Pile shows a considerable reduction in perplexity across various datasets. For instance, GPT-3 (davinci) achieves a perplexity of 5.4508 on the Pile, compared to GPT-2 (xl)'s 11.8633, illustrating a huge stride in LLMing capabilities. This improved performance underscores the efficacy of employing a highly diversified and high-quality training corpus.

Implications for Future Research

From a theoretical perspective, the Pile advances our understanding of how diverse data sources influence the generalization abilities of LLMs. Practically, it provides a robust benchmark for testing cross-domain LLM competence. The release of the dataset and preprocessing code openly invites the research community to explore and extend these findings further.

Ethical and Methodological Considerations

The paper does not shy away from the ethical dimensions of large-scale data collection and usage. The authors document their methodology in detail, highlighting potential biases and the ethical implications of using unstructured web data. They advocate for a more conscientious approach to dataset creation, urging future research to balance comprehensiveness with ethical observance.

Future Directions

Potential future developments include expanding the Pile to be fully multilingual, thereby addressing a significant gap in current NLP resources. Additionally, refining extraction and filtering techniques to encompass a broader range of languages and documents could further enhance the dataset's utility.

Conclusion

The Pile represents a significant step forward in creating a rich, high-quality corpus for training large-scale LLMs. The dataset not only meets the immediate needs for diverse training data but also sets a new standard for future dataset creation in NLP. Its comprehensive coverage across multiple domains, coupled with systematic preprocessing and thorough evaluation, makes it an invaluable resource for advancing LLMing research.