GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation
Kang Min Yoo, Dongju Park, Jaewook Kang, Sang-Woo Lee, Woomyoung Park
Abstract
Large-scale language models such as GPT-3 are excellent few-shot learners, allowing them to be controlled via natural text prompts. Recent studies report that prompt-based direct classification eliminates the need for fine-tuning but lacks data and inference scalability. This paper proposes a novel data augmentation technique that leverages large-scale language models to generate realistic text samples from a mixture of real samples. We also propose utilizing soft-labels predicted by the language models, effectively distilling knowledge from the large-scale language models and creating textual perturbations simultaneously. We perform data augmentation experiments on diverse classification tasks and show that our method hugely outperforms existing text augmentation methods. We also conduct experiments on our newly proposed benchmark to show that the augmentation effect is not only attributed to memorization. Further ablation studies and a qualitative analysis provide more insights into our approach.- Anthology ID:
- 2021.findings-emnlp.192
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2225–2239
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.192
- DOI:
- 10.18653/v1/2021.findings-emnlp.192
- Cite (ACL):
- Kang Min Yoo, Dongju Park, Jaewook Kang, Sang-Woo Lee, and Woomyoung Park. 2021. GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2225–2239, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation (Yoo et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.findings-emnlp.192.pdf
- Code
- naver-ai/hypermix
- Data
- CoLA, MPQA Opinion Corpus, SST