Rewire-then-probe: a contrastive recipe for probing biomedical knowledge of pre-trained language models

Zaiqiao Meng, Fangyu Liu, Ehsan Shareghi, Yixuan Su, Charlotte Collins, Nigel Collier

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Knowledge probing is crucial for understanding the knowledge transfer mechanism behind the pre-trained language models (PLMs). Despite the growing progress of probing knowledge for PLMs in the general domain, specialised areas such as the biomedical domain are vastly under-explored. To facilitate this, we release a well-curated biomedical knowledge probing benchmark, MedLAMA, constructed based on the Unified Medical Language System (UMLS) Metathesaurus. We test a wide spectrum of state-of-the-art PLMs and probing approaches on our benchmark, reaching at most 3% of acc@10. While highlighting various sources of domain-specific challenges that amount to this underwhelming performance, we illustrate that the underlying PLMs have a higher potential for probing tasks. To achieve this, we propose Contrastive-Probe, a novel self-supervised contrastive probing approach, that adjusts the underlying PLMs without using any probing data. While Contrastive-Probe pushes the acc@10 to 28%, the performance gap still remains notable. Our human expert evaluation suggests that the probing performance of our Contrastive-Probe is still under-estimated as UMLS still does not include the full spectrum of factual knowledge. We hope MedLAMA and Contrastive-Probe facilitate further developments of more suited probing techniques for this domain. Our code and dataset are publicly available at https://github.com/cambridgeltl/medlama.
Original languageEnglish
Title of host publicationACL 2022, The 60th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publicationProceedings of the Conference, Vol. 1 (Long Papers)
EditorsDanilo Croce, Ryan Cotterell, Jordan Zhang
Place of PublicationStroudsburg PA USA
PublisherAssociation for Computational Linguistics (ACL)
Pages4798–4810
Number of pages13
ISBN (Electronic)9781955917216
Publication statusPublished - 2022
EventAnnual Meeting of the Association for Computational Linguistics 2022 - Dublin, Ireland
Duration: 22 May 202227 May 2022
Conference number: 60th
https://aclanthology.org/volumes/2022.acl-short/ (Proceedings - Short)
https://aclanthology.org/volumes/2022.acl-long/ (Proceedings - Long)
https://www.2022.aclweb.org/ (Website)

Conference

ConferenceAnnual Meeting of the Association for Computational Linguistics 2022
Abbreviated titleACL 2022
Country/TerritoryIreland
CityDublin
Period22/05/2227/05/22
Internet address

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