LongBEL improves biomedical entity linking consistency by combining full-document context with memory of previous predictions trained via cross-validation rather than gold labels.
Proceedings of the 23rd
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 2years
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UNVERDICTED 2representative citing papers
BeLink applies set-wise instruction-tuning to generative LLMs at the re-ranking stage of biomedical entity linking, reporting 3-24% accuracy gains and reduced inference time versus prior methods.
citing papers explorer
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LongBEL: Long-Context and Document-Consistent Biomedical Entity Linking
LongBEL improves biomedical entity linking consistency by combining full-document context with memory of previous predictions trained via cross-validation rather than gold labels.
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BeLink: Biomedical Entity Linking Meets Generative Re-Ranking
BeLink applies set-wise instruction-tuning to generative LLMs at the re-ranking stage of biomedical entity linking, reporting 3-24% accuracy gains and reduced inference time versus prior methods.