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 learning behavior can be explained by the long-tailed phenomenon . There are two findings: 1) Trained NLU models have strong preference for features located at the head of the long-tailed distribution, and 2) Shortcut features are picked up during very early few iterations of the model training. These two observations are further employed to formulate a measurement which can quantify the shortcut degree of each training sample. Based on this shortcut measurement, we propose a shortcut mitigation framework  , to suppress the model from making overconfident predictions for samples with large shortcut degree. Experimental results on three NLU benchmarks demonstrate that our long-tailed distribution explanation accurately reflects the shortcut learning behavior of NLU models. Experimental analysis further indicates that our method can improve the generalization accuracy on OOD data, while preserving the accuracy on in distribution test data.
Recent studies indicate that NLU models are prone to rely on shortcut features for prediction , without achieving true language understanding . As a result, these models fail to generalize to real-world out-of-distribution data . In this work, we show that the words in the NLU training set can be modeled as a long-tailed distribution . There are two findings: 1) NLU models have strong preference for features located at the head of the long-tailed distribution, and 2) Shortcut features are picked up during very early few iterations of the model training. These two observations are further employed to formulate a measurement which can quantify the shortcut degree of each training sample. Based on this shortcut measurement, we propose a shortcut mitigation framework LGTR , to suppress the model from making overconfident predictions for samples with large shortcut degree. Experimental results on three NLU benchmarks demonstrate that our long-tailed distribution explanation accurately reflects the shortcut learning behavior of NLU models. Experimental analysis further indicates that LGTR can improve the generalization accuracy on OOD data, while preserving the accuracy on in-distribution data.
A
, without achieving true language understanding
D
could potentially
R
scenarios
data
R
shortcut learning behavior can be explained by the
words in the NLU training set can be modeled as a
R
phenomenon
distribution
D
Trained
A
LGTR
R
our method
LGTR
R
in distribution test
in-distribution