{"ID":2857255,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.08902","arxiv_id":"2510.08902","title":"A Unified Biomedical Named Entity Recognition Framework with Large Language Models","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.","short_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 (B...","url_abs":"https://arxiv.org/abs/2510.08902","url_pdf":"https://arxiv.org/pdf/2510.08902v1","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\"]","published":"2025-10-10T01:33:54Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":608432,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2857255,"paper_url":"https://arxiv.org/abs/2510.08902","paper_title":"A Unified Biomedical Named Entity Recognition Framework with Large Language Models","repo_url":"https://github.com/dreamer-tx/LLMNER","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}