A Comprehensive Overview and Analysis of Large Language Models: Trends and Challenges

Large Language Models (LLMs) have transformed numerous fields by offering innovative solutions that drive advancements across a wide range of applications. However, their widespread adoption presents several challenges, including variations in architectures, limitations in processing capabilities, and high computational resource demands for training. Addressing these challenges is crucial for maximizing the benefits of LLMs while ensuring their responsible and efficient use. This paper reviews LLMs, focusing on their key characteristics and the factors that influence their performance. It examines several prominent families of LLMs and provides a comparative analysis of their properties. In addition, it explores the classification of LLMs based on criteria such as availability, context window, and model size. In addition, the study explores advanced fine-tuning techniques, including Parameter-Efficient fine-tuning (PEFT) and Low-Rank Adaptation (LoRA), that enhance the performance and efficiency of models. Furthermore, it reviews the wide-ranging applications of LLMs and evaluates the methodologies used to evaluate their effectiveness.