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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
LLMs: Introduction, Background, Applications, Challenges, Limitations and Future Scope

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

Tyagi, Amit Kumar

Hardcover

Series: Large Language Models for Critical Applications

Technology & EngineeringGeneral Computers

PREORDER - Expected ship date September 29, 2026

ISBN10: 1041298501
ISBN13: 9781041298502
Publisher: CRC Press
Published: Sep 29 2026
Pages: 434
Weight: 1.76
Height: 1.00 Width: 6.14 Depth: 9.21
Language: English

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.

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