Counterfactual Test-Time Augmentation for Shortcut Removal in Language Models.

arXiv:2610.33904v1 Announce Type: cross Abstract: Large language models (LLMs) can exploit shortcuts, i.e., correlations present in training data that may not hold at test time. This can lead to poor predictions when correlations shift. Most existing solutions