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

• URL: http://arxiv.org/abs/2206.15076v1
• Date: Thu, 30 Jun 2022 07:15:45 GMT
• Title: BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
• Authors: Jason Alan Fries, Leon Weber, Natasha Seelam, Gabriel Altay, Debajyoti Datta, Samuele Garda, Myungsun Kang, Ruisi Su, Wojciech Kusa, Samuel Cahyawijaya, Fabio Barth, Simon Ott, Matthias Samwald, Stephen Bach, Stella Biderman, Mario S\"anger, Bo Wang, Alison Callahan, Daniel Le\'on Peri\~n\'an, Th\'eo Gigant, Patrick Haller, Jenny Chim, Jose David Posada, John Michael Giorgi, Karthik Rangasai Sivaraman, Marc P\`amies, Marianna Nezhurina, Robert Martin, Michael Cullan, Moritz Freidank, Nathan Dahlberg, Shubhanshu Mishra, Shamik Bose, Nicholas Michio Broad, Yanis Labrak, Shlok S Deshmukh, Sid Kiblawi, Ayush Singh, Minh Chien Vu, Trishala Neeraj, Jonas Golde, Albert Villanova del Moral, Benjamin Beilharz
• Abstract summary: We introduce BigBIO, a community library of 126+ biomedical NLP datasets. BigBIO facilitates reproducible meta-dataset curation via programmatic access to datasets and their metadata. We discuss our process for task schema, data auditing, contribution guidelines, and outline two illustrative use cases.
• Score: 13.30221348538759
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 diversity of novel pretraining 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 12 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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