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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Presents a scalable system to build a biochemistry knowledge graph (BCKG) from literature and databases, with an application shown for carbohydrate enzymes.
citing papers explorer
-
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.
-
An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries
Presents a scalable system to build a biochemistry knowledge graph (BCKG) from literature and databases, with an application shown for carbohydrate enzymes.