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With the development of large language models (LLMs), zero-shot learning hasattracted much attention for various NLP tasks. Different from prior works thatgenerate training data with billion-scale natural language generation (NLG)models, we propose a retrieval-enhanced framework to create training data froma general-domain unlabeled corpus. To realize this, we first conductcontrastive pretraining to learn an unsupervised dense retriever for extractingthe most relevant documents using class-descriptive verbalizers. We thenfurther propose two simple strategies, namely Verbalizer Augmentation withDemonstrations and Self-consistency Guided Filtering to improve the topiccoverage of the dataset while removing noisy examples. Experiments on ninedatasets demonstrate that REGEN achieves 4.3% gain over the strongest baselinesand saves around 70% of the time compared to baselines using large NLG models.Besides, REGEN can be naturally integrated with recently proposed largelanguage models to boost performance.

ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval