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2022 ACL ACL 2022

Dataset Debt in Biomedical Language Modeling

Abstract

AbstractLarge-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.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
📈 Trend Setter — Clinical NLP
🧭 Keyword Pioneer — biomedical language modeling
🐣 Hot Topic Early Bird — biomedical nlp
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

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

Topics

Machine Learning > Application Areas > Domain Adaptation Natural Language Processing > Resources & Methods > Large Language Models Natural Language Processing > Applications > Clinical NLP Deep Learning > Learning Types > Few-Shot Learning

Keywords

zero-shot learning few-shot learning language modeling dataset curation biomedical nlp data documentation biomedical language modeling
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