  Natural Language Processing (NLP) models optimized for predictive performance
often make high confidence errors and suffer from vulnerability to adversarial
and out-of-distribution data. Existing work has mainly focused on mitigation of
such errors using either humans or an automated approach. In this study, we
explore the usage of large language models (LLMs) for data augmentation as a
potential solution to the issue of NLP models making wrong predictions with
high confidence during classification tasks. We compare the effectiveness of
synthetic data generated by LLMs with that of human data obtained via the same
procedure. For mitigation, humans or LLMs provide natural language
characterizations of high confidence misclassifications to generate synthetic
data, which are then used to extend the training set. We conduct an extensive
evaluation of our approach on three classification tasks and demonstrate its
effectiveness in reducing the number of high confidence misclassifications
present in the model, all while maintaining the same level of accuracy.
Moreover, we find that the cost gap between humans and LLMs surpasses an order
of magnitude, as LLMs attain human-like performance while being more scalable.
