Shortcut Learning

Shortcut learning studies how models come to rely on spurious correlations, such as backgrounds, metadata, speaker identity, or dataset structure, rather than evidence relevant to the task. Researchers diagnose these behaviors with controlled interventions, counterfactual and cross-domain tests, modality ablations, attribution methods, and probes of learned representations. Mitigation approaches include data rebalancing and targeted augmentation, causal or adversarial objectives, feature disentanglement, architectural constraints, and post-training or inference-time interventions. A central challenge is that high accuracy and even readable representations do not establish what drives a decision, while suppressing a shortcut can also remove useful signal or fail when the confounder changes.