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Information-theoretic Probing Explains Reliance on Spurious Features
In this work, we test the hypothesis that the extent to which a feature influences a model's decisions can be predicted using a combination of two factors: The feature's "extractability" after pre-training (measured using information-theoretic probing techniques), and the "evidence" available during fine-tuning (defined as the feature's co-occurrence rate with the label).
Does Data Augmentation Improve Generalization in NLP?
Neural models often exploit superficial features to achieve good performance, rather than deriving more general features.
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