umls

This paper compares two strategies for injecting structured biomedical knowledge from the UMLS Metathesaurus into language models: continual pretraining (embedding knowledge into model parameters) and GraphRAG (querying a knowledge graph at inference time). Results show improvements on biomedical QA benchmarks, with GraphRAG on LLaMA 3-8B yielding over 3 and 5 accuracy points on PubMedQA and BioASQ respectively without any retraining.