Technical Principles of Large Language Models: From Transformer Architectures to Future Challenges

Abstract

With the rapid development of artificial intelligence technologies, Large Language Models (LLMs) have demonstrated exceptional capabilities in language understanding and generation, becoming a central focus in the field of Natural Language Processing (NLP). This paper systematically reviews the fundamental theories and key technical principles of LLMs, with an emphasis on Transformer architectures and self-attention mechanisms. It further analyzes major pretraining methods such as masked language modeling and causal language modeling, and discusses the critical role of fine-tuning and alignment techniques in practical applications. In addition, it introduces parameter expansion and compression strategies, inference optimization methods, and recent advances in enhancing safety and robustness. Finally, the paper explores the future trends of LLMs in multimodal integration, personalized intelligence, and autonomous agent development, and identifies the major efficiency, robustness, and ethical challenges ahead. This review aims to provide a systematic theoretical reference and technical guide for researchers and practitioners, promoting sustained innovation and application expansion of LLM technologies.