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GPT-3 is a large-scale natural language model developed by OpenAI that canperform many different tasks, including topic classification. Althoughresearchers claim that it requires only a small number of in-context examplesto learn a task, in practice GPT-3 requires these training examples to beeither 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 questionis related to data science by augmenting a small training set with additionalexamples generated by GPT-3 itself. This study compares two classifiers: theGPT-3 Classification Endpoint with augmented examples, and the GPT-3 CompletionEndpoint with an optimal training set chosen using a genetic algorithm. We findthat while the augmented Completion Endpoint achieves upwards of 80 percentvalidation accuracy, using the augmented Classification Endpoint yields moreconsistent accuracy on unseen examples. In this way, giving large-scale machinelearning models like GPT-3 the ability to propose their own additional trainingexamples can result in improved classification performance.

Improving Short Text Classification With Augmented Data Using GPT-3