Language rewrites refer to alternative, paraphrased variations of existing text descriptions generated as a form of text data augmentation for machine learning models. Typically produced using large language models, these rewrites introduce diverse sentence structures, phrasing, and vocabulary while strictly preserving the core semantic meaning, key entities, and context of the original source text. By substituting or randomly sampling from these varied descriptions during training alongside the original text, machine learning frameworks—particularly multimodal and vision-language systems—can mitigate overfitting, prevent reliance on textual shortcuts, and improve generalization across diverse real-world language inputs without altering the underlying meaning of the data.
