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

Excerpt

arXiv:2603.05308v4 Announce Type: replace 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 p