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Shamik Bose

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NeurIPS Conference 2022 Conference Paper

BigBio: A Framework for Data-Centric Biomedical Natural Language Processing

  • Jason Fries
  • Leon Weber
  • Natasha Seelam
  • Gabriel Altay
  • Debajyoti Datta
  • Samuele Garda
  • Sunny Kang
  • Rosaline Su

Training and evaluating language models increasingly requires the construction of meta-datasets -- diverse collections of curated data with clear provenance. Natural language prompting has recently lead to improved zero-shot generalization by transforming existing, supervised datasets into a variety of novel instruction tuning tasks, highlighting the benefits of meta-dataset curation. While successful in general-domain text, translating these data-centric approaches to biomedical language modeling remains challenging, as labeled biomedical datasets are significantly underrepresented in popular data hubs. To address this challenge, we introduce BigBio a community library of 126+ biomedical NLP datasets, currently covering 13 task categories and 10+ languages. BigBio facilitates reproducible meta-dataset curation via programmatic access to datasets and their metadata, and is compatible with current platforms for prompt engineering and end-to-end few/zero shot language model evaluation. We discuss our process for task schema harmonization, data auditing, contribution guidelines, and outline two illustrative use cases: zero-shot evaluation of biomedical prompts and large-scale, multi-task learning. BigBio is an ongoing community effort and is available at https: //github. com/bigscience-workshop/biomedical

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NeurIPS Conference 2022 Conference Paper

The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset

  • Hugo Laurençon
  • Lucile Saulnier
  • Thomas Wang
  • Christopher Akiki
  • Albert Villanova del Moral
  • Teven Le Scao
  • Leandro Von Werra
  • Chenghao Mou

As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large language models as a values-driven undertaking, putting issues of ethics, harm, and governance in the foreground. This paper documents the data creation and curation efforts undertaken by BigScience to assemble the Responsible Open-science Open-collaboration Text Sources (ROOTS) corpus, a 1. 6TB dataset spanning 59 languages that was used to train the 176-billion-parameter BigScience Large Open-science Open-access Multilingual (BLOOM) language model. We further release a large initial subset of the corpus and analyses thereof, and hope to empower large-scale monolingual and multilingual modeling projects with both the data and the processing tools, as well as stimulate research around this large multilingual corpus.

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