Year: 2025
Type: conference-paper
Abstract: Existing LLM-based medical questionanswering systems lack citation generation and evaluation capabilities, raising concerns about their adoption in practice.In this work, we introduce MedCite, the first end-to-end framework that facilitates the design and evaluation of citation generation with LLMs for medical tasks.Meanwhile, we introduce a novel multi-pass retrieval-citation method that generates high-quality citations.Our evaluation highlights the challenges and opportunities of citation generation for medical tasks, while identifying important design choices that have a significant impact on the final citation quality.Our proposed method achieves superior citation precision and recall improvements compared to strong baseline methods, and we show that evaluation results correlate well with annotation results from professional experts.
Authors: Xiao Wang, Mengjue Tan, Qiao Jin, Guangzhi Xiong, Yu Hu, Aidong Zhang, Zhiyong Lu, Minjia Zhang
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: G1353381076, G1453181110, G1503004319, Track 4: Advanced CI Coordination Ecosystem: Monitoring and Measurement Services, G2154383771, G298330452, G34258105, G3562386289, Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS), G4010157413, G4275568664, G4403015908, ACO: An Open CI Ecosystem to Advance Scientific Discovery (OpenCI), Category I: Crossing the Divide Between Today's Practice and Tomorrow's Science, Track 3: COre National Ecosystem for CyberinfrasTructure (CONECT), G5156331529, G5367127336, G5464919783, G7526614437, G8108599699, Track 2: Customized Multi-tier Assistance, Training, and Computational Help (MATCH) for End User ACCESS to CI, G8499740153, CAREER: A Framework to Power Next-Generation AI-based Scientific Models on Hardware with Massive Parallelism
OpenAlex ID: W4412888080