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.
CITATION STYLE
Ye, J., Gao, J., Feng, J., Wu, Z., Yu, T., & Kong, L. (2022). PROGEN: Progressive Zero-shot Dataset Generation via In-context Feedback. In Findings of the Association for Computational Linguistics: EMNLP 2022 (pp. 3671–3683). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.findings-emnlp.269
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