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

Yao Ge, Yuting Guo, Sudeshna Das, Abeed Sarker

Abstract read

In one paragraph

Article in NPJ artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Auditing frontier general-purpose large language models in biomedical tasks: reasoning gains, extraction limits, and benchmark reliability.Research square · 2026
Article

4 · The record

Corrections and comments

Update of
Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models.2025

5 · Who and what money

Authors and funding

4 authors.

Yao GeDepartment of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA USA.

Yuting GuoDepartment of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA USA.

Sudeshna DasDepartment of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA USA.

Abeed SarkerDepartment of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA USA.

Funding

Mining Social Media Big Data for Toxicovigilance: Studying Substance Use via Natural Language Processing and Machine Learning MethodsR01DA057599 · NIDA · EMORY UNIVERSITY · PI Abeed H Sarker · 2022 to 2026

$2.2M

NIDA NIH HHS R01 DA057599

6 · The paper itself

Abstract

Biomedical named entity recognition (NER) is a high-utility natural language processing task, and large language models (LLMs) show promise in few-shot settings. In this article, we address performance challenges for few-shot biomedical NER by investigating innovative prompting strategies involving retrieval-augmented generation. Using five biomedical NER datasets, we implemented and evaluated a systematically-structured multi-component static prompt and a dynamic prompt engineering technique, where the prompt is dynamically updated via retrieval with most relevant in-context examples based on the input texts. Static prompting with structured components increased average F

Indexed as

Computational biology and bioinformaticsHealth careMathematics and computing

Identifiers

PMID41929525

DOI10.1038/s44387-025-00062-2

PMCPMC13038409

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.