What question did this study set out to answer?

The aim is to enhance the performance of few-shot named entity recognition in biomedical contexts using structured dynamic prompting.

April 3, 2026npj Artificial IntelligenceOpen Access

Improving few-shot named entity recognition for large language models using structured dynamic prompting with retrieval augmented generation

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Authors

YGYao GeEmory University YGYuting GuoEmory University SDSudeshna DasEmory University

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Implication

This article investigates innovative prompting strategies to enhance performance in few-shot biomedical named entity recognition, suggesting improved efficiency in real-world applications.

Key Points

The aim is to enhance the performance of few-shot named entity recognition in biomedical contexts using structured dynamic prompting.
Evaluated five biomedical NER datasets.
Implemented structured static and dynamic prompt engineering techniques.
Used retrieval-augmented generation for dynamic prompting.
Compared performance across models including GPT-4 and LLaMA 3-70B.
Conducted ablation studies on retrieval pool sizes.
Static prompting increased average F1-scores by 12% for GPT-4 and 11% for other models.
Dynamic prompting improved F1-scores by 8.8% in 5-shot and 6.3% in 10-shot settings.
Small annotation samples yielded strong model performance, demonstrating annotation efficiency.

Cite This Study

Ge et al. (2026) studied this question.

synapsesocial.com/papers/69cf5eee5a333a821460dafa https://doi.org/10.1038/s44387-025-00062-2

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Implication

This article investigates innovative prompting strategies to enhance performance in few-shot biomedical named entity recognition, suggesting improved efficiency in real-world applications.

Improving few-shot named entity recognition for large language models using structured dynamic prompting with retrieval augmented generation

Discussion

Member takes

Implication

Key Points

Cite This Study

Improving few-shot named entity recognition for large language models using structured dynamic prompting with retrieval augmented generation

Discussion

Member takes

Implication

Key Points

Cite This Study

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