Year: 2021
Type: conference-paper
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 taskspecific and require a decent amount of labeled data to start with.Recently, the immense language model GPT-3 with 175 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 with limited labeling budget.These results present a cost-effective data labeling methodology that is generalizable to many practical applications.
Authors: Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, Michael Zeng
Institution: Microsoft (United States)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W3196820561