Unlocking the potential: A comprehensive exploration of large language models in natural language processing

Abstract

In recent years, large language models (LLMs) have revolutionized natural language processing (NLP) with their transformative architectures and sophisticated training techniques. This paper provides a comprehensive overview of LLMs, focusing on their architecture, training methodologies, and diverse applications. We delve into the transformer architecture, attention mechanisms, and parameter tuning strategies that underpin LLMs' capabilities. Furthermore, we explore training techniques such as self-supervised learning, transfer learning, and curriculum learning, highlighting their roles in empowering LLMs with linguistic proficiency. Additionally, we discuss the wide-ranging applications of LLMs, including text generation, sentiment analysis, and question answering, showcasing their versatility and impact across various domains. Through this comprehensive examination, we aim to elucidate the advancements and potentials of LLMs in shaping the future of natural language understanding and generation.