entity-linking

LELA is an LLM-based entity linking framework that combines zero-shot NER and entity disambiguation into an end-to-end Python library, validated across diverse settings.

BeLink introduces a set-wise instruction-tuning formulation for generative re-ranking in biomedical entity linking, achieving 3-24% accuracy improvements and faster inference compared to state-of-the-art systems.

This paper presents a corpus-centric diagnostic framework for analyzing biomedical NER and EL benchmarks, revealing substantial differences across nine corpora and arguing that standard statistics are insufficient for characterizing evaluation demands.