The first preference learning dataset for clinical FHIR extraction with structured error paths.
Structured extraction failures are training signal, not noise.
Unlike traditional datasets that discard generation failures, SGRS-FHIR intentionally captures both valid and invalid extractions with detailed error annotations. This enables:
Direct Preference Optimization (DPO): Train models to… See the full description on the dataset page:
https://huggingface.co/datasets/ai-galileo/clinical-notes-to-fhir.