  In many task settings, text classification models are likely to encounter
examples from novel classes on which they cannot predict correctly. Selective
prediction, in which models abstain on low-confidence examples, provides a
possible solution, but existing models are often overly confident on unseen
classes. To remedy this overconfidence, we introduce Contrastive
Novelty-Augmented Learning (CoNAL), a two-step method that generates OOD
examples representative of novel classes, then trains to decrease confidence on
them. First, we generate OOD examples by prompting a large language model
twice: we prompt it to enumerate relevant novel classes, then generate examples
from each novel class matching the task format. Second, we train a classifier
with a novel contrastive objective that encourages lower confidence on
generated OOD examples than training examples. When trained with CoNAL,
classifiers improve in their ability to detect and abstain on novel class
examples over prior methods by an average of 2.3% in terms of accuracy under
the accuracy-coverage curve (AUAC) and 5.5% AUROC across 4 NLP datasets, with
no cost to in-distribution accuracy.
