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Is GPT-3 a Good Data Annotator?

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arxiv 2212.10450 v2 pith:QH35U777 submitted 2022-12-20 cs.CL

Bosheng Ding , Chengwei Qin , Linlin Liu , Yew Ken Chia , Shafiq Joty , Boyang Li , Lidong Bing This is my paper
classification cs.CL
keywords datagpt-3annotationannotatortasksmodeloutputperformance
verification ladder T0 review T1 audit T2 compute T3 formal
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Data annotation is the process of labeling data that could be used to train machine learning models. Having high-quality annotation is crucial, as it allows the model to learn the relationship between the input data and the desired output. GPT-3, a large-scale language model developed by OpenAI, has demonstrated impressive zero- and few-shot performance on a wide range of NLP tasks. It is therefore natural to wonder whether it can be used to effectively annotate data for NLP tasks. In this paper, we evaluate the performance of GPT-3 as a data annotator by comparing it with traditional data annotation methods and analyzing its output on a range of tasks. Through this analysis, we aim to provide insight into the potential of GPT-3 as a general-purpose data annotator in NLP.

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