Large scale pretrained language models have demonstrated high performance on standard datasets for natural language inference (NLI) tasks. Unfortunately, these evaluations can be misleading, as although the models can perform well on in-distribution data, they perform poorly on out-of-distribution test sets, such as contrast sets. Contrast sets consist of perturbed instances of data that have very minor, but meaningful, changes to the input that alter the gold label, revealing how models can learn superficial patterns in the training data rather than learning more sophisticated language nuances. As an example, the ELECTRA-small language model achieves nearly 90% accuracy on an SNLI dataset but drops to 75% when tested on an out-of-distribution contrast set. The research performed in this study explores how a language models' robustness can be improved by exposing it to small amounts of more complex contrast sets during training to help it better learn language patterns. With this approach, the model regains performance and achieves nearly 90% accuracy on contrast sets, highlighting the importance of diverse and challenging training data.
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