  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 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.
