AugGPT: Leveraging ChatGPT for Text Data Augmentation

TL;DR

AugGPT leverages ChatGPT to generate diverse, faithful, and semantically varied text samples for data augmentation, significantly improving few-shot text classification performance.

Contribution

This paper introduces AugGPT, a novel method using ChatGPT for effective text data augmentation that enhances diversity and faithfulness in few-shot learning scenarios.

Findings

AugGPT outperforms existing augmentation methods in accuracy.

Augmented samples show increased diversity and semantic variation.

Improved model robustness in low-data regimes.

Abstract

Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks. This challenge is especially prominent in the few-shot learning scenario, where the data in the target domain is generally much scarcer and of lowered quality. A natural and widely-used strategy to mitigate such challenges is to perform data augmentation to better capture the data invariance and increase the sample size. However, current text data augmentation methods either can't ensure the correct labeling of the generated data (lacking faithfulness) or can't ensure sufficient diversity in the generated data (lacking compactness), or both. Inspired by the recent success of large language models, especially the development of ChatGPT, which demonstrated improved language comprehension abilities, in this work, we propose a text data augmentation approach based on ChatGPT (named AugGPT). AugGPT rephrases each sentence in the training samples into multiple conceptually similar but semantically different samples. The augmented samples can then be used in downstream model training. Experiment results on few-shot learning text classification tasks show the superior performance of the proposed AugGPT approach over state-of-the-art text data augmentation methods in terms of testing accuracy and distribution of the augmented samples.