GPT-3 is a large-scale natural language model developed by OpenAI that can perform many different tasks, including topic classification. Although researchers claim that it requires only a small number of in-context examples to learn a task, in practice GPT-3 requires these training examples to be either of exceptional quality or a higher quantity than easily created by hand. To address this issue, this study teaches GPT-3 to classify whether a question is related to data science by augmenting a small training set with additional examples generated by GPT-3 itself. This study compares two classifiers: the GPT-3 Classification Endpoint with augmented examples, and the GPT-3 Completion Endpoint with an optimal training set chosen using a genetic algorithm. We find that while the augmented Completion Endpoint achieves upwards of 80 percent validation accuracy, using the augmented Classification Endpoint yields more consistent accuracy on unseen examples. In this way, giving large-scale machine learning models like GPT-3 the ability to propose their own additional training examples can result in improved classification performance.

翻译：GPT-3是一个大型的自然语言模型,由OpenAI公司开发,可以执行许多不同的任务,包括专题分类。研究人员声称,它只需要少量的文本内例子来学习一项任务,但实际上,GPT-3公司要求这些培训例子的质量是特异的,或数量比手工容易生成的要高。为解决这一问题,本研究教GPT-3公司通过增加一个小型培训,加上GPT-3公司本身产生的更多例子,对数据科学问题进行分类。本研究比较了两个分类者:GPT-3分类终点公司增加实例,GPT-3完成点公司采用利用基因算法选择的最佳培训。我们发现,虽然扩大的完成点公司达到80%的鉴定精确度,但利用扩大的分类终点公司在不可见的例子上得出更加一致的准确性。通过这种方式,让诸如GPT-3公司本身的大型机器学习模型能够提出自己的额外培训例子,可以提高分类的性能。