Hindsight-Guided Rationale Distillation for Rare Disease Diagnosis
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Computer Science > Computation and Language
arXiv:2610.03176 (cs)
[Submitted on 2 Oct 2026]
Title:Hindsight-Guided Rationale Distillation for Rare Disease Diagnosis
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Abstract: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 term GT hallucination. Label-visible generation causes the teacher to embed "ground truth is X" phrases in its reasoning chain; SFT copies the pattern. At inference, the unfiltered student reproduces the phrase in 33.9% of cases, with severe accuracy degradation when the hallucinated label is wrong. A regex filter removing these slots reduces contamination to near-zero, producing the observed gain - though the effect remains small. We precisely quantify this gain-cost tradeoff, document frequency-dependent knowledge transfer absent from the RL-trained teacher, and characterize a calibration gap that SFT does not close - identifying both as directions for future work.
| Comments: | 15 pages, 4 figures, Github: this https URL, Accepted at AACL-IJCNLP SRW 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.03176 [cs.CL] |
| (or arXiv:2610.03176v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03176
arXiv-issued DOI via DataCite (pending registration)
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