Empirical Risk Minimization(ERM)models often rely on spurious correlations between features and labels during the learning process,leading to shortcut learning behavior that undermines robustness generalization perfor...Empirical Risk Minimization(ERM)models often rely on spurious correlations between features and labels during the learning process,leading to shortcut learning behavior that undermines robustness generalization performance.Current research mainly targets identifying or mitigating a single shortcut;however,in real-world scenarios,cues within the data are diverse and unknown.In empirical studies,we reveal that models rely more on strong shortcuts than weak ones,with their performance under multiple shortcuts typically falling between that of an individual shortcut.To address these challenges,we propose MiMu,a novel method integrated with Transformer-based ERMs designed to Mitigate Multiple shortcut learning behavior,which incorporates self-calibration strategy and self-improvement strategy.In the source model,we first propose the self-calibration strategy to prevent the model from relying on shortcuts and make overconfident predictions.Then,we design self-improvement strategy in target model to further reduce the reliance on multiple shortcuts.The random mask strategy involves randomly masking partial attention positions to diversify the focus of target model avoiding fixation on a fixed region.Meanwhile,the adaptive attention alignment module facilitates the alignment of attention weights to the calibrated source model,without the need for post-hoc attention maps or supervision.Finally,extensive experiments conducted on Natural Language Processing(NLP)and Computer Vision(CV)demonstrate the effectiveness of MiMu in improving the robustness generalization abilities.展开更多
文摘Empirical Risk Minimization(ERM)models often rely on spurious correlations between features and labels during the learning process,leading to shortcut learning behavior that undermines robustness generalization performance.Current research mainly targets identifying or mitigating a single shortcut;however,in real-world scenarios,cues within the data are diverse and unknown.In empirical studies,we reveal that models rely more on strong shortcuts than weak ones,with their performance under multiple shortcuts typically falling between that of an individual shortcut.To address these challenges,we propose MiMu,a novel method integrated with Transformer-based ERMs designed to Mitigate Multiple shortcut learning behavior,which incorporates self-calibration strategy and self-improvement strategy.In the source model,we first propose the self-calibration strategy to prevent the model from relying on shortcuts and make overconfident predictions.Then,we design self-improvement strategy in target model to further reduce the reliance on multiple shortcuts.The random mask strategy involves randomly masking partial attention positions to diversify the focus of target model avoiding fixation on a fixed region.Meanwhile,the adaptive attention alignment module facilitates the alignment of attention weights to the calibrated source model,without the need for post-hoc attention maps or supervision.Finally,extensive experiments conducted on Natural Language Processing(NLP)and Computer Vision(CV)demonstrate the effectiveness of MiMu in improving the robustness generalization abilities.
