  This work focuses on in-context data augmentation for intent detection.
Having found that augmentation via in-context prompting of large pre-trained
language models (PLMs) alone does not improve performance, we introduce a novel
approach based on PLMs and pointwise V-information (PVI), a metric that can
measure the usefulness of a datapoint for training a model. Our method first
fine-tunes a PLM on a small seed of training data and then synthesizes new
datapoints - utterances that correspond to given intents. It then employs
intent-aware filtering, based on PVI, to remove datapoints that are not helpful
to the downstream intent classifier. Our method is thus able to leverage the
expressive power of large language models to produce diverse training data.
Empirical results demonstrate that our method can produce synthetic training
data that achieve state-of-the-art performance on three challenging intent
detection datasets under few-shot settings (1.28% absolute improvement in
5-shot and 1.18% absolute in 10-shot, on average) and perform on par with the
state-of-the-art in full-shot settings (within 0.01% absolute, on average).
