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Submit a ProposalLarge Language Models and Architecture Evolution
One Line Description
Master the theoretical mechanics and real-world deployment of modern AI with this definitive guide, giving you the exact technical depth needed to build, evaluate, and innovate with state-of-the-art Large Language Model systems.
Description
In the last decade, the landscape of natural language processing has shifted dramatically, evolving from foundational architectures into complex, high-impact systems driving technological transformation across industries. This volume provides a comprehensive exploration of Large Language Models (LLMs), bridging foundational mechanics with state-of-the-art advances for undergraduate and graduate students, researchers, and technical professionals. Organized into three cohesive sections, the book traces the trajectory of LLMs from fundamental underlying mechanisms to advanced paradigms, including Retrieval-Augmented Generation (RAG), long-context modeling, and industry-standard integrations like DeepSeek and AutoGPT. Beyond core theoretical principles and real-world case studies, it delivers a critical analysis of current system limitations, safety bottlenecks, and practical deployment constraints. Serving as a definitive reference for academia and industry alike, this text equips readers with the holistic understanding required to evaluate, innovate, and deploy modern LLM technologies effectively.
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In the last decade, the landscape of natural language processing has shifted dramatically, evolving from foundational architectures into complex, high-impact systems driving technological transformation across industries. This volume provides a comprehensive exploration of Large Language Models (LLMs), bridging foundational mechanics with state-of-the-art advances for undergraduate and graduate students, researchers, and technical professionals. Organized into three cohesive sections, the book traces the trajectory of LLMs from fundamental underlying mechanisms to advanced paradigms, including Retrieval-Augmented Generation (RAG), long-context modeling, and industry-standard integrations like DeepSeek and AutoGPT. Beyond core theoretical principles and real-world case studies, it delivers a critical analysis of current system limitations, safety bottlenecks, and practical deployment constraints. Serving as a definitive reference for academia and industry alike, this text equips readers with the holistic understanding required to evaluate, innovate, and deploy modern LLM technologies effectively.
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Author / Editor Details
Krishan Arora, PhD is the Head of the Department of Power Systems in the School of Electronics and Electrical Engineering at Lovely Professional University, India, with more than seventeen years of experience in academics and research. He has published more than 85 research papers in refereed journals and conferences, five edited books, six Indian patents, and a copyright. He has edited 5 books in different areas of Electronics and Electrical engineering. His expertise lies in power electronics, non-conventional energy sources, electric drives, induction and synchronous machines, and digital electronics.
Krishan Arora, PhD is the Head of the Department of Power Systems in the School of Electronics and Electrical Engineering at Lovely Professional University, India, with more than seventeen years of experience in academics and research. He has published more than 85 research papers in refereed journals and conferences, five edited books, six Indian patents, and a copyright. He has edited 5 books in different areas of Electronics and Electrical engineering. His expertise lies in power electronics, non-conventional energy sources, electric drives, induction and synchronous machines, and digital electronics.
Balraj Singh, PhD is a Professor in the School of Computer Science and Engineering at Lovely Professional University, India. He has published more than 40 research papers in various journals, conferences, and book chapters, as well as several patents. His research areas include distributed systems, software engineering, and networks.
Suman Lata Tripathi, PhD is a Professor at Lovely Professional University, India, with more than 20 years of experience in academics. She has published more than 74 research papers in refereed journals and conferences, 17 books, 13 Indian patents, and two copyrights. Her area of expertise includes microelectronics device modeling and characterization, low-power VLSI circuit design, VLSI design of testing, and advanced FET design for IoT.
Mufti Mahmud is a Professor in the Department of Information and Computer Science at the King Fahd University of Petroleum and Minerals, Saudi Arabia. He has an extensive international research profile, with more than 350 peer-reviewed publications and substantial research funding. His research expertise includes cognitive computing, artificial intelligence in healthcare, neuroengineering, brain informatics, and responsible AI.
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