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Want To Reduce Labeling Cost? GPT-3 Can Help

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arxiv 2108.13487 v1 pith:437CQS27 submitted 2021-08-30 cs.CL cs.AI

Shuohang Wang , Yang Liu , Yichong Xu , Chenguang Zhu , Michael Zeng This is my paper · ORCID
classification cs.CLcs.AI
keywords datagpt-3labelslabelingmanytasksmodelperformance
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 4 Pith papers

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