August 25, 2025

Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models

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Dynamic prompting enhances performance in few-shot biomedical named entity recognition, suggesting effective retrieval strategies.

Dynamic prompting improved F1-scores in biomedical named entity recognition, showing promise for LLMs in few-shot tasks.
Static prompting with structured components increased average F1-scores by 12% for GPT-4, showcasing its effectiveness in this context.
Retrieval methods like TF-IDF and SBERT achieved the best improvements in F1-scores, indicating the value of effective retrieval strategies.
These findings emphasize the potential of contextually adaptive prompts via retrieval-augmented generation for enhancing NER performance.

Ge et al. (2025) studied this question.

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