Learn from A Rationalist: Distilling Intermediate Interpretable Rationales

arXiv:2601.22531v2 Announce Type: replace Abstract: Because of the pervasive use of deep neural networks (DNNs), especially in high-stakes domains, the interpretability of DNNs has received increased attention. The general idea of rationale extraction (RE) is to provide an interpretable-by-design framework for DNNs via a select-predict architecture where two neural networks learn jointly to perform feature selection and prediction, respectively. Given only the remote supervision from the final task prediction, the process of learning to select subsets of features (or rationales) requires searc

Source: arXiv cs.LG — read the full report at the original publisher.

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