Want To Reduce Labeling Cost? GPT-3 Can Help
Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, Michael Zeng
Abstract
Data annotation is a time-consuming and labor-intensive process for many NLP tasks. Although there exist various methods to produce pseudo data labels, they are often task-specific and require a decent amount of labeled data to start with. Recently, the immense language model GPT-3 with 170 billion parameters has achieved tremendous improvement across many few-shot learning tasks. In this paper, we explore ways to leverage GPT-3 as a low-cost data labeler to train other models. We find that to make the downstream model achieve the same performance on a variety of NLU and NLG tasks, it costs 50% to 96% less to use labels from GPT-3 than using labels from humans. Furthermore, we propose a novel framework of combining pseudo labels from GPT-3 with human labels, which leads to even better performance. These results present a cost-effective data labeling methodology that is generalizable to many practical applications.
BibTeX
@inproceedings{wang-etal-2021-want-reduce,
title = "Want To Reduce Labeling Cost? {GPT}-3 Can Help",
author = "Wang, Shuohang and
Liu, Yang and
Xu, Yichong and
Zhu, Chenguang and
Zeng, Michael",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.354/",
doi = "10.18653/v1/2021.findings-emnlp.354",
pages = "4195--4205"
}