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        <title>BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels</title>
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        <namePart type="given">Mengfei</namePart>
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    <abstract>Effective biomedical information retrieval requires modeling domain semantics and hierarchical relationships among biomedical texts. Existing biomedical generative retrievers built on coarse binary relevance signals, limiting their ability to capture semantic overlap. We propose BioHiCL - Biomedical Retrieval with Hierarchical Multi-Label Contrastive Learning, which leverages hierarchical MeSH annotations to provide structured supervision for multi-label contrastive learning. Our models, BioHiCL-Base (0.1B) and BioHiCL-Large (0.3B), achieve promising performance on biomedical retrieval, sentence similarity, and question answering tasks, while remaining computationally efficient for deployment.</abstract>
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%0 Conference Proceedings
%T BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels
%A Lan, Mengfei
%A Zheng, Lecheng
%A Kilicoglu, Halil
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-391-3
%F lan-etal-2026-biohicl
%X Effective biomedical information retrieval requires modeling domain semantics and hierarchical relationships among biomedical texts. Existing biomedical generative retrievers built on coarse binary relevance signals, limiting their ability to capture semantic overlap. We propose BioHiCL - Biomedical Retrieval with Hierarchical Multi-Label Contrastive Learning, which leverages hierarchical MeSH annotations to provide structured supervision for multi-label contrastive learning. Our models, BioHiCL-Base (0.1B) and BioHiCL-Large (0.3B), achieve promising performance on biomedical retrieval, sentence similarity, and question answering tasks, while remaining computationally efficient for deployment.
%U https://aclanthology.org/2026.acl-short.62/
%P 759-769

Markdown (Informal)

[BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels](https://aclanthology.org/2026.acl-short.62/) (Lan et al., ACL 2026)

• BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels (Lan et al., ACL 2026)

ACL

• Mengfei Lan, Lecheng Zheng, and Halil Kilicoglu. 2026. BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 759–769, San Diego, California, United States. Association for Computational Linguistics.