Abstract
Pre-trained language models (LMs) have become ubiquitous in solving various natural language processing (NLP) tasks. There has been increasing interest in what knowledge these LMs contain and how we can extract that knowledge, treating LMs as knowledge bases (KBs). While there has been much work on probing LMs in the general domain, there has been little attention to whether these powerful LMs can be used as domain-specific KBs. To this end, we create the BIOLAMA benchmark, which is comprised of 49K biomedical factual knowledge triples for probing biomedical LMs. We find that biomedical LMs with recently proposed probing methods can achieve up to 18.51% Acc@5 on retrieving biomedical knowledge. Although this seems promising given the task difficulty, our detailed analyses reveal that most predictions are highly correlated with prompt templates without any subjects, hence producing similar results on each relation and hindering their capabilities to be used as domain-specific KBs. We hope that BIOLAMA can serve as a challenging benchmark for biomedical factual probing.
Original language | English |
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Title of host publication | EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 4723-4734 |
Number of pages | 12 |
ISBN (Electronic) | 9781955917094 |
Publication status | Published - 2021 |
Event | 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 - Virtual, Punta Cana, Dominican Republic Duration: 2021 Nov 7 → 2021 Nov 11 |
Publication series
Name | EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings |
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Conference
Conference | 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 |
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Country/Territory | Dominican Republic |
City | Virtual, Punta Cana |
Period | 21/11/7 → 21/11/11 |
Bibliographical note
Funding Information:This work was supported in part by the ICT Creative Consilience program (IITP-2021-0-01819) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation), National Research Foundation of Korea (NRF-2020R1A2C3010638, NRF-2014M3C9A3063541), and Hyundai Motor Chung Mong-Koo Foundation. We thank the anonymous reviewers for their insightful comments.
Publisher Copyright:
© 2021 Association for Computational Linguistics
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Science Applications
- Information Systems