[2603.05308] Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution

Computer Science > Computation and Language arXiv:2603.05308 (cs) [Submitted on 5 Mar 2026] Title:Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution Authors:Qiao Jin, Yin Fang, Lauren He, Yifan Yang, Guangzhi Xiong, Zhizheng Wang, Nicholas Wan, Joey Chan, Donald C. Comeau, Robert Leaman, Charalampos S. Floudas, Aidong Zhang, Michael F. Chiang, Yifan Peng, Zhiyong Lu View a PDF of the paper titled Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution, by Qiao Jin and 14 other authors View PDF HTML (experimental) Abstract:Assessing whether an article supports an assertion is essential for hallucination detection and claim verification. While large language models (LLMs) have the potential to automate this task, achieving strong performance requires frontier models such as GPT-5 that are prohibitively expensive to deploy at scale. To efficiently perform biomedical evidence attribution, we present Med-V1, a family of small language models with only three billion parameters. Trained on high-quality synthetic data newly developed in this study, Med-V1 substantially outperforms (+27.0% to +71.3%) its base models on five biomedical benchmarks unified into a verification format. Despite its smaller size, Med-V1 performs comparably to frontier LLMs such as GPT-5, along with high-quality explanations for its predictions. We use Med-V1 to conduct a first-of-its-kind use case study that quantifies hallucinations i...