Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction 事件

Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction arXiv:2606.10279v1 Announce Type: cross Abstract: Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching models not just what to predict but why. We test this assumption on five-year Alzheimer's disease and related dementias (ADRD) prediction from longitudinal health histories. Across a large-scale controlled expe