Counterfactual Test-Time Augmentation for Shortcut Removal in Language Models
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 for removing shortcuts operate at training time, requiring labeled training data, model weight access, and si...
Katie Matton, R. Picard, John V. Guttag et al.
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