论文标题
GPT-3是一个好的数据注释器吗？
Is GPT-3 a Good Data Annotator?
论文作者
论文摘要
数据注释是标记可用于训练机器学习模型的数据的过程。具有高质量的注释至关重要，因为它允许模型学习输入数据与所需输出之间的关系。 GPT-3是由OpenAI开发的大型语言模型，在各种NLP任务上表现出令人印象深刻的零和很少的性能。因此，很自然地怀疑它是否可以用于有效地注释NLP任务的数据。在本文中，我们通过将GPT-3作为数据注释的性能与传统数据注释方法进行比较，并在一系列任务上分析其输出。通过此分析，我们旨在深入了解GPT-3作为NLP中通用数据注释的潜力。
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.
