Hindsight-Guided Rationale Distillation for Rare Disease Diagnosis
We study hindsight-guided distillation for rare disease diagnosis on ZebraMap: a 1.5B student is fine-tuned on chain-of-thought traces from a 8B teacher that observes the ground-truth diagnosis during generation. Absolute accuracy remains low for all models - the task is hard at this scale - but within this ceiling a filtered variant (StudentF) achieves a small, statistically significant accuracy advantage over the teacher (p < 0.001), concentrated in better-represented diseases. The unfiltered student does not significantly outperform the teacher (p = 0.129), establishing that contamination filtering - not hindsight distillation alone - drives the gain. The gap traces to an artifact we t...
| Authors | Aarav Singh, Animesh Pathak, Navyansh Singh |
|---|---|
| Primary category | cs.CL |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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