BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
This addresses the challenge of data-centric approaches in biomedical NLP for researchers and practitioners, though it is incremental as it builds on existing meta-dataset curation methods.
The authors tackled the underrepresentation of biomedical NLP datasets in data hubs by introducing BigBIO, a community library of 126+ datasets across 12 task categories and 10+ languages, which facilitates reproducible meta-dataset curation and enables zero-shot evaluation and multi-task learning.
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