Rethinking Synthetic Rationale Supervision for Disease Prediction
The study tested using synthetic rationales to improve Alzheimer's disease prediction by teaching models 'why' they predict a certain outcome. The results showed that training models on these generated rationales consistently and substantially hurt their predictive performance compared to training with only the final labels. This suggests that conditioning predictions on synthetic explanations degrades accuracy.