Study proposes perturbation-sensitive selection for medical QA rationales

A new arXiv paper addresses the scarcity of high-quality rationales in medical question-answering datasets, where answer labels are plentiful but explanations are expensive to validate. The authors reframe the data acquisition problem as deciding which already-labeled questions warrant rationales, using a perturbation-sensitive selection criterion. The approach aims to improve QA robustness by targeting rationale annotation where it has the greatest effect.