09-23-2026, 03:18 PM
LLMs: Introduction, Background, Applications, Challenges, Limitations and Future Scope, edited by Amit Kumar Tyagi, is a broad overview of Large Language Models, covering their foundations, transformer architectures, development from traditional NLP, and major capabilities such as text generation, translation, summarization, coding, and conversational systems.
The 446-page book brings together chapters on LLM applications across fields including aerospace, agriculture, healthcare, finance, cybersecurity, enterprise automation, and Industry 5.0. It also discusses newer approaches such as multimodal AI, retrieval-augmented generation (RAG), virtual assistants, and lightweight models for edge applications.
A substantial part of the book focuses on the limitations and future development of LLMs. It identifies problems including hallucinations, bias, privacy and data-security concerns, high computational and energy costs, dependence on training-data quality, and limitations in genuine reasoning.
The book also covers evaluation metrics and benchmarks and looks toward improvements in efficiency, explainability, domain adaptation, multimodal capabilities, and more reliable context-aware AI systems. Overall, it is structured as a multidisciplinary survey rather than a narrowly technical treatment, making it an overview of both the technology and its expanding applications and challenges.
BOOK
The 446-page book brings together chapters on LLM applications across fields including aerospace, agriculture, healthcare, finance, cybersecurity, enterprise automation, and Industry 5.0. It also discusses newer approaches such as multimodal AI, retrieval-augmented generation (RAG), virtual assistants, and lightweight models for edge applications.
A substantial part of the book focuses on the limitations and future development of LLMs. It identifies problems including hallucinations, bias, privacy and data-security concerns, high computational and energy costs, dependence on training-data quality, and limitations in genuine reasoning.
The book also covers evaluation metrics and benchmarks and looks toward improvements in efficiency, explainability, domain adaptation, multimodal capabilities, and more reliable context-aware AI systems. Overall, it is structured as a multidisciplinary survey rather than a narrowly technical treatment, making it an overview of both the technology and its expanding applications and challenges.
BOOK
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