Understanding the Technical Foundations of Large Language Models: Architectures, Training, and Applications

Abstract

This in-depth paper on Large Language Models (LLMs) delves into their technical foundations, architectures, and uses in contemporary artificial intelligence. Starting with a precursor to transformer architectures and self-attention mechanism, the paper critiques how these developments have transformed natural language processing abilities. It delves into the computational requirements and scaling laws that govern LLM training, highlighting the relationship between model size, dataset characteristics, and performance outcomes. The article further investigates tokenization methodologies, embedding techniques, and context window innovations that enable efficient text processing. Advanced adaptation strategies, including fine-tuning approaches, instruction tuning, reinforcement learning from human feedback, and prompt engineering techniques, are evaluated for their effectiveness in customizing LLMs for specific domains and applications. Throughout the analysis, the article emphasizes both the technical advances and practical implications of these technologies across diverse fields.