关键词: 数据增强方法, 大语言模型, 自然语言处理, 深度学习, 人工智能

Abstract: Currently, large language models show great potential in the field of natural language processing (NLP), but their training process relies on a large number of high-quality samples. In low-resource scenarios, the number of existing data samples can hardly support the convergence of model training as the model size keeps increasing, and this problem has inspired researchers in related fields to investigate data augmentation methods. However, traditional data enhancement methods have limited application scope and data distortion problems in the context of large models in NLP. In contrast, data enhancement methods based on large language models can address this challenge more effectively. This paper comprehensively explores the current stage of large language model data enhancement methods in the NLP domain and adopts a comprehensive perspective to study data enhancement in the NLP domain. Firstly, the development history of traditional data enhancement methods and big language models in the NLP domain is reviewed. Then, a variety of large language model data enhancement methods in the NLP domain at this stage are summarised, and the scope of application, advantages and limitations of each method are discussed in depth. Subsequently, data enhancement evaluation methods in the field of NLP are introduced. Finally, future research directions of data enhancement methods for large language models in the NLP domain are discussed through comparative experiments and result analyses of current methods, and prospective suggestions are made. The goal of this paper is to provide key insights to those working on big language modelling in the NLP domain, and ultimately to promote the development of big language modelling in the field.

Key words: data augmentation, large language models, natural language processing, deep learning, artificial intelligence