BeLink: Biomedical Entity Linking Meets Generative Re-Ranking
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Computer Science > Computation and Language
Title:BeLink: Biomedical Entity Linking Meets Generative Re-Ranking
Abstract:Despite recent progress, Biomedical Entity Linking (BEL) with large language models (LLMs) remains computationally inefficient and challenging to deploy in practical settings. In this work, we demonstrate that instruction-tuning of open-source generative models can offer an effective solution when applied at the re-ranking stage of the BEL pipeline. We propose a set-wise instruction-tuning formulation that enables fast and accurate candidate selection. Our method demonstrates strong performance on multiple BEL benchmarks, yielding significant improvements in linking accuracy (3%-24%) while reducing inference time compared to the state-of-the-art. We integrate our generative re-ranker into BeLink, a modular, end-to-end system designed for practical real-world BEL applications.
| Comments: | Accepted to ACM SIGIR 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2605.22501 [cs.CL] |
| (or arXiv:2605.22501v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22501
arXiv-issued DOI via DataCite (pending registration)
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| Related DOI: | https://doi.org/10.1145/3805712.3809918
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