LLMs

Introduction, Background, Applications, Challenges, Limitations and Future Scope

Amit Kumar Tyagi

• Publisher: CRC Press
• Year: 2026
• Print ISBN:
  9781041298502
• Edition: 1
• eBook ISBN:
  9781040939178
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

In today's era, Large Language Models (LLMs) are advanced AI systems that are trained on large amounts of text data, to understand and generate human-like language. These systems built on transformer architectures have evolved from traditional NLP (Natural Language Processing) models to powerful tools that enable tasks like translation, summarization, coding, and conversational agents, etc., to make human life easier and convenient. Today we have different types of LLMs models in different areas to automate tasks, make predictions or perform tasks with help of AI. Today's LLMs are widely used in different sectors like healthcare, education, finance, and cybersecurity, etc. However, LLMs face several challenges like bias, hallucination, high computational cost, and data privacy issues. Also, some limitations include a lack of true reasoning and dependence on training data quality. With this, some future scope lies with improving explainability, efficiency, domain adaptation, and integrating multimodal capabilities for more reliable and context-aware intelligent systems.

Table of contents

• Cover
• Half Title
• Series
• Title
• Copyright
• Preface to the Series
• Preface
• Acknowledgement
• Contents
• 1. Large Language Models (LLMs): Foundation and Architectures
• 2. Core Components and Architectures of LLMs
• 3. Large Language Models (LLMs) in Aerospace: Use Cases and Applications
• 4. Large Language Models in Agriculture: Applications, Challenges, and Future Directions
• 5. The Rise of Transformer Architecture and Deep Learning in NLP
• 6. Integration of LLMs with Multimodal AI Systems
• 7. Next-Generation Virtual Assistants Powered by Multi-Modal Retrieval-Augmented Generation
• 8. Large Language Models for Text Generation: Foundations, Architectures, Applications, and Social Impacts
• 9. Fault Detection and Localization at Network Edges Using Lightweight Machine Learning Models
• 10. Enterprise Applications: Automation, Analytics, and Knowledge 233 Management
• 11. Leveraging Large Language Models for Fraud Detection and Risk Assessment in Financial Institutions—A Comprehensive Review
• 12. The Fusion of Quantum Intelligence and Large Language Models for Future Smart Healthcare
• 13. Large Language Models in Industry 5.0: Fundamental and Applications
• 14. Evaluation Metrics and Benchmarks for LLM Performance
• 15. Challenges and Limitations in LLMs
• 16. A New Framework for Bibliometric Network Analysis: Methodology, Implementation, and Case Study
• 17. Deep Bibliometrics: Integrating Machine Learning for Enhanced Citation and Co-Authorship Analysis
• 18. Generating Question Answer-Pairs from a Given Set of Educational Text Using Transformer-Based Models
• Index