Context-aware Explainable Selective Regression

Regression models can have low average error yet make large errors on individual samples, which can lead to suboptimal decisions. Selective regression mitigates this issue by rejecting unreliable predictions, but existing methods rely primarily on numerical data. Text associated with each sample can carry signals of unreliability that numerical data do not capture, and can thus provide evidence for rejection. We introduce *context-aware explainable selective regression*, a post-hoc framework that equips a fixed regressor with a context-aware rejector. Given a numerical input, the associated text, and the prediction from the regressor, the rejector outputs a score for threshold-based rejection. For each rejected sample, it also extracts evidence from the text and generates rationales explaining why the evidence indicates low reliability. We implement the rejector with a fine-tuned large language model. Across 16 real-world datasets, it outperforms existing methods on average, with clear gains from text on tabular data and domain-dependent effects on time-series data. While the evidence is only partially faithful to the scoring behavior of the rejector, the evidence and rationales appear plausible in case studies, which suggests their potential to support human review.