文献类型：期刊文献

英文题名：DBEE: Dual-Path Biomedical Event Extraction withLarge Language Model

作者：Zeng, Jianjun[1]; Wang, Jiacheng[1]; Zhang, Weiyan[1]; Wang, Yunpeng[1]; Zhou, Yan[1]; Wu, Yinan[1]; Zhu, Lifeng[2]; Ruan, Tong[1]; Liu, Jingping[3]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Ruijin Hospital, Shanghai Jiao Tong University Medical School, Shanghai, 200025, China; [3] School of Software Engineering, Sun Yat-sen University, Guangdong, Zhuhai, 519000, China

年份：2026

卷号：16540 LNCS

起止页码：153

外文期刊名：Lecture Notes in Computer Science

收录：EI(收录号：20262220815243)

语种：英文

外文关键词：Biomedical engineering - Computational linguistics - Large datasets - Tuning

摘要：Biomedical Event Extraction (BEE) aims to extract structured information from biomedical texts to support downstream applications. While Large Language Models(LLMs) have shown promise in this task, current methods suffer from hallucinations due to the generative nature and the limited size of BEE datasets for effective instruction tuning. To this end, we propose Dual-path Biomedical Event Extraction (DBEE), a novel framework that mitigates these issues via dual-path consistent extraction, and discrepancy retention. Specifically, DBEE consists of two components: (1) a Chain-of-Thought (CoT) enriched instruction design and data augmentation that reforms the event extraction process into two paths with cross-verification, and (2) discrepancy retention module to recall plausible events from inconsistent predictions. Extensive experiments on Chinese and English BEE datasets show that DBEE achieves state-of-the-art performance. The code is open-sourced and available at https://github.com/zengjianjun-ecust/DBEE. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.