Auggpt: Leveraging Chatgpt For Text Data Augmentation

Dai Haixing, Liu Zhengliang, Liao Wenxiong, Huang Xiaoke, Cao Yihan, Wu Zihao, Zhao Lin, Xu Shaochen, Liu Wei, Liu Ninghao, Li Sheng, Zhu Dajiang, Cai Hongmin, Sun Lichao, Li Quanzheng, Shen Dinggang, Liu Tianming, Li Xiang. Arxiv 2023

[Paper]    
Few Shot GPT Model Architecture RAG Training Techniques

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.

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