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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.192.pdf
Code
 naver-ai/hypermix
Data
CoLAMPQA Opinion CorpusSST