A Unified Biomedical Named Entity Recognition Framework with Large Language Models

• URL: http://arxiv.org/abs/2510.08902v1
• Date: Fri, 10 Oct 2025 01:33:54 GMT
• Title: A Unified Biomedical Named Entity Recognition Framework with Large Language Models
• Authors: Tengxiao Lv, Ling Luo, Juntao Li, Yanhua Wang, Yuchen Pan, Chao Liu, Yanan Wang, Yan Jiang, Huiyi Lv, Yuanyuan Sun, Jian Wang, Hongfei Lin,
• Abstract summary: We propose a unified Biomedical Named Entity Recognition (BioNER) framework based on Large Language Models (LLMs)<n>We first reformulate BioNER as a text generation task and design a symbolic tagging strategy to jointly handle both flat and nested entities.<n>We perform bilingual joint fine-tuning across multiple Chinese and English datasets.
• Score: 44.92744341698289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate recognition of biomedical named entities is critical for medical information extraction and knowledge discovery. However, existing methods often struggle with nested entities, entity boundary ambiguity, and cross-lingual generalization. In this paper, we propose a unified Biomedical Named Entity Recognition (BioNER) framework based on Large Language Models (LLMs). We first reformulate BioNER as a text generation task and design a symbolic tagging strategy to jointly handle both flat and nested entities with explicit boundary annotation. To enhance multilingual and multi-task generalization, we perform bilingual joint fine-tuning across multiple Chinese and English datasets. Additionally, we introduce a contrastive learning-based entity selector that filters incorrect or spurious predictions by leveraging boundary-sensitive positive and negative samples. Experimental results on four benchmark datasets and two unseen corpora show that our method achieves state-of-the-art performance and robust zero-shot generalization across languages. The source codes are freely available at https://github.com/dreamer-tx/LLMNER.

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