All You Should Know About Large Language Models (LLMs)

Abstract

This article examines recent advancements in Large Language Models (LLMs), focusing on efficiency improvements and practical implementation. Through analysis of literature from 2021 to present, we identify key innovations making these models more accessible. We highlight three main areas of progress: enhanced Transformer architectures enabling longer text processing with fewer resources, novel fine-tuning techniques for efficient task adaptation, and expanded applications beyond language processing into vision, speech, and reinforcement learning. The review evaluates efficiency innovations including patternbased approaches and dynamic attention mechanisms, comparing their benefits and trade-offs. Our analysis provides insights for researchers and practitioners working to make LLMs more practical and accessible, particularly addressing deployment challenges like memory and computational constraints.