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

• URL: http://arxiv.org/abs/2305.10703v1
• Date: Thu, 18 May 2023 04:30:09 GMT
• Title: ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval
• Authors: Yue Yu, Yuchen Zhuang, Rongzhi Zhang, Yu Meng, Jiaming Shen, Chao Zhang
• Abstract summary: We propose a retrieval-enhanced framework to create training data from a general-domain unlabeled corpus. Experiments on nine datasets demonstrate that REGEN achieves 4.3% gain over the strongest baselines and saves around 70% of the time compared to baselines using large NLG models.
• Score: 22.882301169283323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of large language models (LLMs), zero-shot learning has attracted much attention for various NLP tasks. Different from prior works that generate training data with billion-scale natural language generation (NLG) models, we propose a retrieval-enhanced framework to create training data from a general-domain unlabeled corpus. To realize this, we first conduct contrastive pretraining to learn an unsupervised dense retriever for extracting the most relevant documents using class-descriptive verbalizers. We then further propose two simple strategies, namely Verbalizer Augmentation with Demonstrations and Self-consistency Guided Filtering to improve the topic coverage of the dataset while removing noisy examples. Experiments on nine datasets demonstrate that REGEN achieves 4.3% gain over the strongest baselines and saves around 70% of the time compared to baselines using large NLG models. Besides, REGEN can be naturally integrated with recently proposed large language models to boost performance.

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