Efficient medical NER with limited data: Enhancing LLM performance through annotation guidelines

Including annotation guidelines in prompts enhances the performance of LLMs in NER tasks, making this a practical approach for developing accurate NLP systems in resource-constrained environments. Although annotation guidelines are essential for evaluation and example creation, their integration into LLM prompts can further optimize few-shot learning, especially within specialized domains such as medical NLP.

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Publication Details

Journal
International Journal of Medical Informatics
Published
2025-12-19
DOI
https://doi.org/10.1016/j.ijmedinf.2025.106230
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
0.00
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article

Efficient medical NER with limited data: Enhancing LLM performance through annotation guidelines

Yoshimasa Kawazoe, Emiko Shinohara
International Journal of Medical Informatics
Topic Modeling
article

Efficient medical NER with limited data: Enhancing LLM performance through annotation guidelines

Yoshimasa Kawazoe, Emiko Shinohara
article en

Abstract

Including annotation guidelines in prompts enhances the performance of LLMs in NER tasks, making this a practical approach for developing accurate NLP systems in resource-constrained environments. Although annotation guidelines are essential for evaluation and example creation, their integration into LLM prompts can further optimize few-shot learning, especially within specialized domains such as medical NLP.

International Journal of Medical InformaticsVol. 208
Tokyo Health Care University (JP), The University of Tokyo (JP)
Openalex Percentile: Top 78%
Topic Modeling
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