论文标题
使用GPT-3通过增强数据改进短文分类
Improving Short Text Classification With Augmented Data Using GPT-3
论文作者
论文摘要
GPT-3是由OpenAI开发的大规模自然语言模型，可以执行许多不同的任务，包括主题分类。尽管研究人员声称，仅需少数文本示例即可学习任务，但实际上，GPT-3要求这些培训示例是出色的质量或更高的数量，而不是手工创建的示例。为了解决这个问题，这项研究教会GPT-3通过增强小型培训设置，并使用GPT-3本身生成的其他示例来对问题进行分类。这项研究将两个分类器与增强示例进行了比较：GPT-3分类端点，GPT-3完成端点与使用遗传算法选择的最佳训练集。我们发现，尽管增强的完成端点实现了80％以上的验证精度，但使用增强的分类端点在看不见的示例中可以更加一致。这样，提供大规模的机器学习模型（例如GPT-3）提出自己的额外培训示例的能力可以改善分类性能。
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.
