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.

通过将大型语言模型（LLMs）应用于生物医学命名实体识别（NER）任务，将 NER 任务分解为实体跨度提取和实体类型确定两个步骤，同时注入实体知识以解决 LLM 在预测实体类别时缺乏领域知识的问题，实验证明了我们的两步 BioNER 方法相对于之前的少样本 LLM 基线在性能上有显著提高，而引入外部知识则显著增强了实体类别确定性能。