Text Transformation

Text transformation research focuses on modifying textual data to improve various NLP tasks, such as enhancing model training, evaluating large language models (LLMs), and improving the efficiency of text-based applications. Current research explores diverse techniques, including graph-based representations, self-supervised learning with data augmentation and format transforms, and novel algorithms for efficient compression of transformer models. These advancements aim to address challenges like data scarcity, model interpretability, and computational cost, ultimately leading to more robust and effective NLP systems across domains including healthcare and robotics.