LLMs in Biomedicine: A study on clinical Named Entity Recognition
<|reference_start|>LLMs in Biomedicine: A study on clinical Named Entity Recognition: Large Language Models (LLMs) demonstrate remarkable versatility in various NLP tasks but encounter distinct challenges in biomedical due to the complexities of language and data scarcity. This paper investigates LLMs application in the biomedical domain by exploring strategies to enhance their performance for the NER task. Our study reveals the importance of meticulously designed prompts in the biomedical. Strategic selection of in-context examples yields a marked improvement, offering ~15-20\% increase in F1 score across all benchmark datasets for biomedical few-shot NER. Additionally, our results indicate that integrating external biomedical knowledge via prompting strategies can enhance the proficiency of general-purpose LLMs to meet the specialized needs of biomedical NER. Leveraging a medical knowledge base, our proposed method, DiRAG, inspired by Retrieval-Augmented Generation (RAG), can boost the zero-shot F1 score of LLMs for biomedical NER. Code is released at \url{https://github.com/masoud-monajati/LLM_Bio_NER}<|reference_end|>
@article{monajatipoor2024llms,
    title={LLMs in Biomedicine: A study on clinical Named Entity Recognition},
    author={Masoud Monajatipoor, Jiaxin Yang, Joel Stremmel, Melika Emami,
  Fazlolah Mohaghegh, Mozhdeh Rouhsedaghat, Kai-Wei Chang},
    journal={arXiv preprint arXiv:2404.07376},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07376},
    primaryClass={cs.CL}
}