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BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels

arXiv 2604.15591 ↗First posted 17 Apr 2026cs.IR cs.AIcited by 0

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Abstract (arXiv)

Effective biomedical information retrieval requires modeling domain semantics and hierarchical relationships among biomedical texts. Existing biomedical generative retrievers build 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 38 references that are in the map. First posted on arXiv 17 Apr 2026.

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19 of 38 references.

References · 19

  1. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
  2. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,288
  3. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
  4. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  5. 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 1,009
  6. 2023C-Pack: Packed Resources For General Chinese Embeddingscited by 578
  7. 2021Large Dual Encoders Are Generalizable Retrieverscited by 456
  8. 2020SPECTER: Document-level Representation Learning using Citation-informed Transformerscited by 430
  9. 2022SGPT: GPT Sentence Embeddings for Semantic Searchcited by 204
  10. 2023MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrievalcited by 185
  11. 2022SciFact-Open: Towards open-domain scientific claim verificationcited by 94
  12. 2024SPLADE-v3: New baselines for SPLADEcited by 72
7 more
  1. 2018BioSentVec: creating sentence embeddings for biomedical textscited by 47
  2. 2024BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrieverscited by 38
  3. 2023Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Trainingcited by 35
  4. 2024Heterogeneous Contrastive Learning for Foundation Models and Beyondcited by 27
  5. 2019BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scalecited by 8
  6. 2018A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrievalcited by 3
  7. 2025BiCA: Effective Biomedical Dense Retrieval with Citation-Aware Hard Negativescited by 3

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