  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.
