Understanding the Architecture and Functionality of Large Language Models in Modern AI

Abstract

Large language models (LLMs) represent a significant advancement in the field of artificial intelligence (AI), demonstrating remarkable capabilities in natural language understanding, generation, and various other language-related tasks. This paper delves into the architecture and functionality of LLMs, exploring their foundational principles, operational mechanisms, and the technological innovations that have driven their development. We examine key models, such as GPT-4, BERT, and T5, highlighting their unique features and contributions to the field. Additionally, we discuss the implications of LLMs on AI applications, including their potential to transform industries, enhance human-computer interaction, and address complex challenges in data processing. By providing a comprehensive understanding of LLMs, this paper aims to inform future research and development efforts, fostering advancements that leverage these models' strengths while addressing their limitations.