Inspire the Large Language Model by External Knowledge on BioMedical
  Named Entity Recognition
Junyi Bian Jiaxuan Zheng Yuyi Zhang Shanfeng Zhu
Large language models (LLMs) have demonstrated dominating performance in many
NLP tasks, especially on generative tasks. However, they often fall short in
some information extraction tasks, particularly those requiring domain-specific
knowledge, such as Biomedical Named Entity Recognition (NER). In this paper,
inspired by Chain-of-thought, we leverage the LLM to solve the Biomedical NER
step-by-step: break down the NER task into entity span extraction and entity
type determination. Additionally, for entity type determination, we inject
entity knowledge to address the problem that LLM's lack of domain knowledge
when predicting entity category. Experimental results show a significant
improvement in our two-step BioNER approach compared to previous few-shot LLM
baseline. Additionally, the incorporation of external knowledge significantly
enhances entity category determination performance.