LLMs Introduction, Background, Applications, Challenges, Limitations and Future Scope
Date: 11 September 2026 | Posted by: BeMyLove
LLMs Introduction, Background, Applications, Challenges, Limitations and Future Scope | 35.23 MB
Title: LLMs Introduction, Background, Applications, Challenges, Limitations and Future Scope
Author: Amit Kumar Tyagi
Category: Artificial Intelligence, General Computing, Computers
Language: English | 447 Pages | ISBN: 9781040939178
Description:
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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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.
DOWNLOAD:
https://rapidgator.net/file/0c3a1c8764f5fb217e0af53e26e0c870/LLMs_Introduction_Background_Applications_Challenges_Limitations_and_Future_Scope.rar
https://nitroflare.com/view/65AEE00D7DDA84E/LLMs_Introduction_Background_Applications_Challenges_Limitations_and_Future_Scope.rar
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