BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels (2026.acl-short)
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| Challenge: | Existing biomedical generative retrievers lack domain semantics and hierarchical relationships among biomedically related texts. |
| Approach: | They propose a biomedical retrieval model with hierarchical multi-label contrastive learning that leverages hierarchic MeSH annotations to provide structured supervision for multi-labor contrastive training. |
| Outcome: | The proposed models achieve promising performance on biomedical retrieval, sentence similarity, and question answering tasks while remaining computationally efficient for deployment. |
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