  Recent studies have revealed the intriguing few-shot learning ability of
pretrained language models (PLMs): They can quickly adapt to a new task when
fine-tuned on a small amount of labeled data formulated as prompts, without
requiring abundant task-specific annotations. Despite their promising
performance, most existing few-shot approaches that only learn from the small
training set still underperform fully supervised training by nontrivial
margins. In this work, we study few-shot learning with PLMs from a different
perspective: We first tune an autoregressive PLM on the few-shot samples and
then use it as a generator to synthesize a large amount of novel training
samples which augment the original training set. To encourage the generator to
produce label-discriminative samples, we train it via weighted maximum
likelihood where the weight of each token is automatically adjusted based on a
discriminative meta-learning objective. A classification PLM can then be
fine-tuned on both the few-shot and the synthetic samples with regularization
for better generalization and stability. Our approach FewGen achieves an
overall better result across seven classification tasks of the GLUE benchmark
than existing few-shot learning methods, improving no-augmentation methods by
5+ average points, and outperforming augmentation methods by 3+ average points.
