arXiv:2605.22501v1 Announce Type: new 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 imp
The increasing sophistication of large language models is being applied to specialized domain challenges like biomedical entity linking, seeking to overcome previous computational inefficiencies.
Improving the efficiency and accuracy of biomedical entity linking through advanced AI can accelerate medical research, drug discovery, and clinical decision support.
Instruction-tuning of generative models is shown to offer an effective and computationally efficient solution for a critical natural language processing task in the biomedical field.
- · Biomedical research institutions
- · Pharmaceutical companies
- · AI developers specializing in NLP
- · Developers of less efficient, traditional BEL systems
Biomedical entity linking becomes more accessible and efficient with generative AI.
Faster and more accurate analysis of vast datasets of biomedical literature and clinical notes.
Accelerated discovery of new disease pathways, drug targets, and personalized treatment strategies.
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