The Emergence of a New Computational Paradigm in Natural Language Processing: A Review of Architectures, Adaptation, and Applications of Large Language Models

Large Language Models have become a transformational element in Natural Language Processing because they introduce new approach for understanding and generating languages. This paper is a formal review of the development of Large Language Models from a variety of perspectives, including the architectural advances, pre training strategies, and adaptation techniques. The paper focuses on the process of moving from early contextual word representations to large scale transformer based systems trained using very large collections of written language, describing significant advancements in model architecture, pretraining methods, and techniques to adapt the models for future tasks. Furthermore, the major applications, including text summarization, translation, dialogue systems, information extraction,and question answering are discussed. The paper further analyzes critical challenges such as computational scalability, data requirements, model alignment, inference efficiency, ethical concerns, and deployment limitations.