ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback
Jiacheng Ye, Jiahui Gao, Zhiyong Wu, Jiangtao Feng, Tao Yu, Lingpeng Kong
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 being suffering from the low-quality issue (e.g., low informativeness, 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 in 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, when comparing to baseline methods without in-context feedback.- Anthology ID:
- 2022.findings-emnlp.269
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3671–3683
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.269
- DOI:
- 10.18653/v1/2022.findings-emnlp.269
- Cite (ACL):
- Jiacheng Ye, Jiahui Gao, Zhiyong Wu, Jiangtao Feng, Tao Yu, and Lingpeng Kong. 2022. ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3671–3683, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback (Ye et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2022.findings-emnlp.269.pdf