Bridging the Knowledge Gap: Improving BERT models for answering MCQs by using Ontology-generated synthetic MCQA Dataset
BERT-based models possess impressive language understanding capabilities but often lack domain-specific knowledge, limiting their performance on specialised tasks such as medical multiple-choice question answering (MCQA). In this paper, we study how biomedical ontologies, rich repositories of medica...
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| Principais autores: | , |
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| Format: | Artigo |
| Jezik: | Inglês |
| Izdano: |
LibraryPress@UF
2024-05-01
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| Serija: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Teme: | |
| Online dostop: | https://journals.flvc.org/FLAIRS/article/view/135608 |
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