Dataset Debt in Biomedical Language Modeling

Jason Fries, Natasha Seelam, Gabriel Altay, Leon Weber, Myungsun Kang, Debajyoti Datta, Ruisi Su, Samuele Garda, Bo Wang, Simon Ott, Matthias Samwald, Wojciech Kusa


Abstract
Large-scale language modeling and natural language prompting have demonstrated exciting capabilities for few and zero shot learning in NLP. However, translating these successes to specialized domains such as biomedicine remains challenging, due in part to biomedical NLP’s significant dataset debt – the technical costs associated with data that are not consistently documented or easily incorporated into popular machine learning frameworks at scale. To assess this debt, we crowdsourced curation of datasheets for 167 biomedical datasets. We find that only 13% of datasets are available via programmatic access and 30% lack any documentation on licensing and permitted reuse. Our dataset catalog is available at: https://tinyurl.com/bigbio22.
Anthology ID:
2022.bigscience-1.10
Volume:
Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models
Month:
May
Year:
2022
Address:
virtual+Dublin
Venues:
ACL | BigScience
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
137–145
Language:
URL:
https://aclanthology.org/2022.bigscience-1.10
DOI:
10.18653/v1/2022.bigscience-1.10
Bibkey:
Cite (ACL):
Jason Fries, Natasha Seelam, Gabriel Altay, Leon Weber, Myungsun Kang, Debajyoti Datta, Ruisi Su, Samuele Garda, Bo Wang, Simon Ott, Matthias Samwald, and Wojciech Kusa. 2022. Dataset Debt in Biomedical Language Modeling. In Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models, pages 137–145, virtual+Dublin. Association for Computational Linguistics.
Cite (Informal):
Dataset Debt in Biomedical Language Modeling (Fries et al., BigScience 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.bigscience-1.10.pdf
Data
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