Yao Qin
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Large pre-trained language models have shown remarkable performance over the past few years. These models, however, sometimes learn superficial features from the dataset and cannot generalize to the distributions that are dissimilar to the training scenario. There have been several approaches proposed to reduce model's reliance on these bias features which can improve model robustness in the out-of-distribution setting. However, existing methods usually use a fixed low-capacity model to deal with various bias features, which ignore the learnability of those features. In this paper, we analyze a set of existing bias features and demonstrate there is no single model that works best for all the cases. We further show that by choosing an appropriate bias model, we can obtain a better robustness result than baselines with a more sophisticated model design.
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Neural networks lack adversarial robustness, ie, they are vulnerable to adversarial examples that through small perturbations to inputs cause incorrect predictions. Further, trust is undermined when models give miscalibrated predictions, ie, the predicted probability is not a good indicator of how much we should trust our model. In this paper, we study the connection between adversarial robustness and calibration and find that the inputs for which the model is sensitive to small perturbations (are easily attacked) are more likely to have poorly calibrated predictions. Based on this insight, we examine if calibration can be improved by addressing those adversarially unrobust inputs. To this end, we propose Adversarial Robustness based Adaptive Label Smoothing (AR-AdaLS) that integrates the correlations of adversarial robustness and calibration into training by adaptively softening labels for an example based on how easily it can be attacked by an adversary. We find that our method, taking the adversarial robustness of the in-distribution data into consideration, leads to better calibration over the model even under distributional shifts. In addition, AR-AdaLS can also be applied to an ensemble model to further improve model calibration.
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NLP models are shown to suffer from robustness issues, for example, a model's prediction can be easily changed under small perturbations to the input. In this work, we aim to present a Controlled Adversarial Text Generation (CAT-Gen) model that, given an input text, it can generate adversarial texts through controllable attributes that are known to be invariant to task labels. For example, for a main task like sentiment classification, an example attribute can be different categories/domains, and a model should have similar performance across them; for a coreference resolution task, a model's performance should not differ across different demographic attributes. Different from many existing adversarial text generation approaches, we show that our model can generate adversarial texts that are more fluent, diverse, and with better task-label invariance guarantees. We aim to use this model to generate counterfactual texts that could better improve robustness in NLP models (e.g., through adversarial training), and we argue that our generation can create more natural attacks.
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