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.

翻译：摘要：有效的生物医学信息检索需要建模生物医学文本的领域语义及层次关系。现有生物医学生成式检索器依赖于粗粒度的二值相关性信号，这限制了其捕获语义重叠的能力。我们提出BioHiCL（基于分层多标签对比学习的生物医学检索），该方法利用分层的MeSH注释为多标签对比学习提供结构化监督。我们的模型BioHiCL-Base（0.1B）和BioHiCL-Large（0.3B）在生物医学检索、句子相似度及问答任务上取得了卓越性能，同时保持了计算效率，便于实际部署。