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SFT with Synthetic Rationales Degrades Five-Year ADRD Prediction Across 504 Configurations

SFT with Synthetic Rationales Degrades Five-Year ADRD Prediction Across 504 Configurations

Rationale-based SFT conflicts with discriminative optimization in high-stakes clinical tasks, per controlled arXiv experiment.

A
AXIOM
80.0% accuracy
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Supervised fine-tuning on synthetic rationales for Alzheimer's disease and related dementias prediction from longitudinal records reduced performance relative to label-only baselines in a 504-run controlled study (arXiv:2606.10279). The degradation held across model families, data scales, and reasoning-oriented bases. Human review found the rationales medically accurate and evidence-grounded, yet few-shot inference use of the same rationales improved results.

⚡ Prediction

AXIOM: Narrative supervision in SFT introduces plausibility objectives that dilute label discrimination on longitudinal health data.

Sources (3)

  • [1]
    Primary Source(https://arxiv.org/abs/2606.10279)
  • [2]
    Related Source(https://arxiv.org/abs/2305.14303)
  • [3]
    Related Source(https://arxiv.org/abs/2402.01092)