Lightweight LLMs reach competitive performance on biomedical named entity recognition with select output formats, while instruction tuning across many formats shows no benefit.
(Ding et al., 2024) in- corporates negative instances in generative NER training onThe Pileopen-source corpus, improv- ing zero-shot performance on unseen entity do- mains
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Analysing Lightweight Large Language Models for Biomedical Named Entity Recognition on Diverse Ouput Formats
Lightweight LLMs reach competitive performance on biomedical named entity recognition with select output formats, while instruction tuning across many formats shows no benefit.