DEMYSTIFYING LARGE LANGUAGE MODELS: A TECHNICAL DEEP DIVE
DOI:
https://doi.org/10.34218/IJCET_16_01_180Keywords:
Transformer Architecture, Attention Mechanisms, Neural Language Models, Multimodal Learning, Computational EfficiencyAbstract
This article provides a comprehensive exploration of Large Language Models (LLMs), examining their fundamental architectures, training methodologies, and future directions. Beginning with the revolutionary transformer architecture, it delves into the core components that enable these models to process and generate human language at scale. The article examines key innovations in tokenization strategies, embedding techniques, and pre-training approaches that have contributed to the remarkable capabilities of modern LLMs. It analyzes the evolution from basic attention mechanisms to sophisticated multi-head architectures, discussing how these developments have enhanced model performance across various natural language processing tasks. The article also investigates advanced concepts such as attention head diversity and layer normalization, providing insights into their roles in model stability and effectiveness. Finally, it explores emerging trends in multimodal integration and efficiency improvements, highlighting how sparse architectures and cross-modal learning are shaping the future of language models. Throughout, the article emphasizes the practical implications of these technological advances for researchers and practitioners in the field.
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