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This work focuses on in-context data augmentation for intent detection.Having found that augmentation via in-context prompting of large pre-trainedlanguage models (PLMs) alone does not improve performance, we introduce a novelapproach based on PLMs and pointwise V-information (PVI), a metric that canmeasure the usefulness of a datapoint for training a model. Our method firstfine-tunes a PLM on a small seed of training data and then synthesizes newdatapoints - utterances that correspond to given intents. It then employsintent-aware filtering, based on PVI, to remove datapoints that are not helpfulto the downstream intent classifier. Our method is thus able to leverage theexpressive power of large language models to produce diverse training data.Empirical results demonstrate that our method can produce synthetic trainingdata that achieve state-of-the-art performance on three challenging intentdetection datasets under few-shot settings (1.28% absolute improvement in5-shot and 1.18% absolute in 10-shot, on average) and perform on par with thestate-of-the-art in full-shot settings (within 0.01% absolute, on average).

Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information