ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback

• URL: http://arxiv.org/abs/2210.12329v1
• Date: Sat, 22 Oct 2022 02:07:10 GMT
• Title: ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback
• Authors: Jiacheng Ye, Jiahui Gao, Jiangtao Feng, Zhiyong Wu, Tao Yu, Lingpeng Kong
• Abstract summary: We propose a progressive zero-shot dataset generation framework, ProGen, to guide the generation of new training data. We show ProGen achieves on-par or superior performance with only 1% synthetic dataset size.
• Score: 21.168991554983815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, dataset-generation-based zero-shot learning has shown promising results by training a task-specific model with a dataset synthesized from large pre-trained language models (PLMs). The final task-specific model often achieves compatible or even better performance than PLMs under the zero-shot setting, with orders of magnitude fewer parameters. However, synthetic datasets have their drawbacks. They have long been suffering from low-quality issues (e.g., low informativeness and redundancy). This explains why the massive synthetic data does not lead to better performance -- a scenario we would expect in the human-labeled data. To improve the quality of dataset synthesis, we propose a progressive zero-shot dataset generation framework, ProGen, which leverages the feedback from the task-specific model to guide the generation of new training data via in-context examples. Extensive experiments on five text classification datasets demonstrate the effectiveness of the proposed approach. We also show ProGen achieves on-par or superior performance with only 1\% synthetic dataset size compared to baseline methods without in-context feedback.

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