Unfolding the Epoch of Large Language Models: A Comprehensive Exploration

Abstract

Natural Language Processing (NLP) has undergone a remarkable transformation since the introduction of Large Language Models (LLMs). The extensive scope and sophisticated architectures of these models have redefined the limits of machine comprehension and generation of human language. This paper provides a comprehensive review of the evolution, architecture, and applications of LLMs, tracing their development from simple statistical models to advanced Transformer-based architectures such as GPT-4. We strive to provide a comprehensive understanding of the current state of LLMs through a thorough examination of historical context and significant milestones. In addition, we conduct a comprehensive analysis of the architectural principles regulating these models and investigate a multidimensional taxonomy encompassing the various aspects of LLMs. To cast light on the broader implications of this technology, the transformative impact of LLMs across a variety of sectors is examined. Finally, we look to the future and discuss potential directions for future advancements in this field, emphasising the need for interdisciplinary approaches and automated classification. This paper aims to promote a comprehensive understanding of LLMs, thereby contributing to the scholarly discourse and assisting practitioners and policymakers in navigating the rapidly evolving landscape of NLP and AI.