Recognizing out-of-distribution (OOD) samples is critical for machine learning systems deployed in the open world. The vast majority of OOD detection methods are driven by a single modality (e.g., either vision or language), leaving the rich …
Recent studies indicate that NLU models are prone to rely on shortcut features for prediction. As a result, these models could potentially fail to generalize to real-world out-of-distribution scenarios. In this work, we show that the shortcut …