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 experiment of 504 configurations, we find that rationale-based SFT consistently and substantially hurts prediction performance relative to label-only fine-tuning. The degradation persists across
This research provides a direct challenge to a widely held assumption in AI development, highlighting a critical flaw in current supervised fine-tuning methodologies for clinical tasks.
This finding indicates that a common technique thought to enhance AI interpretability and performance (rationale-based SFT) can actually degrade predictive accuracy in sensitive areas like healthcare, demanding a re-evaluation of current practices.
The conventional wisdom that injecting synthetic rationale data universally improves medical AI model performance is now directly challenged, necessitating a focus on alternative or more nuanced fine-tuning strategies.
- · AI safety researchers
- · Healthcare AI developers focusing on robust, explainable AI
- · Patients benefiting from more accurate diagnostic AI
- · Developers solely relying on rationale-based SFT
- · Companies advocating for simple, rationale-driven AI adoption in healthcare
- · Early-stage clinical AI models using this fine-tuning approach
AI developers will re-evaluate and likely reduce reliance on synthetic rationale data for supervised fine-tuning in critical prediction tasks.
Increased investment and research will be directed towards alternative methods for improving AI explainability and performance that do not compromise accuracy.
New regulatory frameworks for AI in healthcare may emerge, specifically scrutinizing the methodologies used for training and fine-tuning models, especially concerning interpretability vs. accuracy trade-offs.
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Read at arXiv cs.CL