Abstract: The rapid evolution of Large Language Models (LLMs) has transformed the field of Natural Language Processing (NLP), enabling machines to understand, generate, and interact with human language at unprecedented levels of sophistication. This article explores the chronological development, underlying architectures, and transformative capabilities of LLMs such as GPT, BERT, and PaLM, while highlighting their influence across key domains including healthcare, education, finance, and customer service. The study synthesizes insights from recent literature to examine how scaling model parameters, integrating multimodal data, and enhancing contextual reasoning have expanded the functional boundaries of NLP. Moreover, it discusses current challenges related to computational efficiency, ethical concerns, and model interpretability, proposing pathways for future research that focus on sustainability, transparency, and alignment with human values. Ultimately, this review underscores the immense potential of LLMs to redefine human–machine communication and drive innovation across industries in the era of intelligent automation.

Keywords: Large Language Models (LLMs); Natural Language Processing (NLP); Deep Learning; Transformer Architecture; Model Scaling; Multimodal Learning; AI Ethics; Computational Efficiency; Human–AI Interaction; Future of Artificial Intelligence.

Downloads: | DOI: 10.17148/IJIREEICE.2024.12317