Papers
arxiv:2304.14334

ZeroShotDataAug: Generating and Augmenting Training Data with ChatGPT

Published on Apr 27, 2023
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Abstract

ChatGPT-generated synthetic training data outperforms existing methods in data augmentation for low-resource scenarios, and methodologies are proposed to evaluate the quality of this augmented data.

AI-generated summary

In this paper, we investigate the use of data obtained from prompting a large generative language model, ChatGPT, to generate synthetic training data with the aim of augmenting data in low resource scenarios. We show that with appropriate task-specific ChatGPT prompts, we outperform the most popular existing approaches for such data augmentation. Furthermore, we investigate methodologies for evaluating the similarity of the augmented data generated from ChatGPT with the aim of validating and assessing the quality of the data generated.

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