山西大学学报（自然科学版）2026，Vol.49Issue(4)：547-556,10.DOI:10.13451/j.sxu.ns.2025118
大模型的药物不良反应实体识别能力评估
Evaluation of Large Language Models for Adverse Drug Reaction Entity Recognition
摘要
Abstract
This study focused on the named entity recognition task in the field of adverse drug reactions and constructed a Chinese dataset named Chinese named entity recognition pharmacovigilance dataset(CNER-PH).Based on this dataset,mainstream named entity recognition(NER)models and large language models were systematically evaluated.The methods used included several pre-trained models such as bidirectional encoder representations from transformers(BERT)series and robustly optimized BERT pretrain-ing approach(RoBERTa),as well as large language models such as GPT-4o and DeepSeek.The analysis emphasized their recogni-tion ability under conditions of diverse entity types and unclear entity boundaries.The experimental results show that BERT series models are more stable in handling complex entity boundaries,and their overall F1 scores are higher than those of large language models.The findings reveal the strengths and weaknesses of different models in adverse drug reaction recognition and provide a ba-sis for future methodological improvements.关键词
大语言模型/药物警戒/命名实体识别/数据集构建Key words
large language models/pharmacovigilance/named entity recognition/dataset construction分类
信息技术与安全科学引用本文复制引用
王子天,徐康,董振江..大模型的药物不良反应实体识别能力评估[J].山西大学学报（自然科学版）,2026,49(4):547-556,10.基金项目
国家自然科学基金(62202240 （）
61872190 （）
62276142) （）
江苏省社会发展项目(BE2023025) （BE2023025）
国家社会科学基金重点项目(23ATQ009) （23ATQ009）
江苏省社会科学基金(23TQB007) （23TQB007）
江苏省高校哲学社会科学研究重大项目(2020SJZDA102) （2020SJZDA102）
