Biomed-Enriched: Data-Efficient Biomedical Pretraining via Paragraph-Level Annotation

摘要

We annotate PubMed Central paragraphs for document type, domain, and educational quality using a two-stage pipeline: Llama-3.1-70B labels 400K paragraphs, then a fine-tuned XLM-RoBERTa propagates annotations to the full corpus. This paragraph-level approach captures content diversity within scientific articles that document-level labels miss. The resulting Biomed-Enriched corpus contains 2M clinical case paragraphs, providing a publicly available alternative to restricted clinical datasets. For decoders, continual pretraining experiments enable targeted improvements, with clinical upsampling boosting performance by 4 points on MMLU ProfMed and educational filtering improving MedQA and MedMCQA by ~1 point. Combinations of these techniques led to faster convergence, reaching the same performance with a third of training tokens. For encoders, our best recipe matches BioClinical-ModernBERT on 11 tasks (77.3% vs 77.1% F1) while using 2.5x fewer tokens and only public data.