Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution
#Med-V1 #small language models #biomedical evidence #zero-shot learning #scalable attribution #AI in healthcare #evidence linking
📌 Key Takeaways
- Med-V1 is a small language model designed for biomedical evidence attribution.
- It enables zero-shot learning, requiring no task-specific training data.
- The model aims to scale biomedical evidence attribution efficiently.
- It focuses on linking biomedical claims to supporting evidence.
📖 Full Retelling
arXiv:2603.05308v1 Announce Type: cross
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 pa
🏷️ Themes
Biomedical AI, Language Models
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Mentioned Entities
Original Source
--> Computer Science > Computation and Language arXiv:2603.05308 [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 Abstract: Assessing whether an article supports an assertion is essential for hallucination detection and claim verification. While large language models 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 in LLM-generated answers under different citation instructions. Results show that the format instruction strongly affects citation validity and hallucination, with GPT-5 generating more claims but exhibiting hallucination rates similar to GPT-4o. Additionally, we present a second use case showing that Med-V1 can automatically identify high-stakes evidence misattributions in clinical practice guidelines, revealing potentially negative public health impacts that are otherwise challenging to identify at scal...
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