Training and evaluating language models increasingly requires the construction ofmeta-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 introduceBigBIOa community library of 126+ biomedical NLP datasets, currently covering 12 task categories and 10+ languages.BigBIOfacilitates 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.BigBIOis an ongoing community effort and is available atthis URL.