Text Augmentation

Text augmentation enhances the performance of natural language processing (NLP) models by artificially increasing the size and diversity of training datasets. Current research focuses on leveraging large language models (LLMs) to generate high-quality augmentations, addressing challenges like information loss and semantic drift through techniques such as question-answer pair generation, paraphrasing, and contextual synonym replacement. These advancements are significant because they improve the robustness and accuracy of NLP models across various tasks, including text classification, question answering, and machine translation, particularly in low-resource settings.