Across 504 configurations on five-year ADRD prediction, rationale-based supervised fine-tuning consistently degrades performance relative to label-only fine-tuning, despite high-quality rationales validated by experts.
InFindings of the Association for Computational Linguistics: ACL 2025, Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar (Eds.)
2 Pith papers cite this work. Polarity classification is still indexing.
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Symptom Induction compresses labeled data into interpretable guidelines that improve LLM classification of depression symptoms in text, outperforming zero-shot, in-context, and fine-tuning approaches with gains on rare symptoms and cross-disease generalization.
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Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction
Across 504 configurations on five-year ADRD prediction, rationale-based supervised fine-tuning consistently degrades performance relative to label-only fine-tuning, despite high-quality rationales validated by experts.
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Learning Evidence of Depression Symptoms via Prompt Induction
Symptom Induction compresses labeled data into interpretable guidelines that improve LLM classification of depression symptoms in text, outperforming zero-shot, in-context, and fine-tuning approaches with gains on rare symptoms and cross-disease generalization.