Multi-Vector Biomedical Dense Retrieval with Knowledge-Enhanced Entity-Type Clustering

Single-vector dense retrieval models, which are foundational to modern Retrieval-Augmented Generation (RAG) systems, struggle to represent the multifaceted semantics of complex documents, particularly in specialized fields like biomedicine. This semantic bottleneck limits their ability to provide comprehensive context for generation tasks. To address this, we propose ELK-Multi, a novel multi-vector retrieval framework that constructs fine-grained document representations through knowledge-enhanced entity-type clustering. By leveraging a knowledge-aware encoder, ELK-Multi first identifies and groups entities by their type, generating a distinct vector for each semantic cluster. These targeted representations are then combined with a global document vector using principled aggregation strategies to balance fine-grained detail with holistic context. Extensive experiments on the TREC-COVID and NFCorpus datasets validate our approach, where ELK-Multi establishes new state-of-the-art results in NDCG and Recall. This is complemented by a detailed efficiency analysis demonstrating that our model achieves this performance while remaining within the efficient dual-encoder paradigm, alongside a qualitative analysis with case studies and visualizations.