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Challenges and Applications of Generative Large Language Models

2026-06-13 分类：土木工程 评论(0)

Challenges and Applications of Generative Large Language Models book cover

Challenges and Applications of Generative Large Language Models

Author(s): Anitha S. Pillai Ph.D. (Editor), Roberto Tedesco Ph.D. (Editor), Vincenzo Scotti Ph.D. (Editor)

  • Publisher: Morgan Kaufmann
  • Publication Date: January 23, 2026
  • Edition: 1st
  • Language: English
  • Print length: 270 pages
  • ISBN-10: 0443335923
  • ISBN-13: 9780443335921

Book Description

Large Language Models (LLMs) are a form of generative AI, based on Deep Learning, that rely on very large textual datasets, and are composed of hundreds of millions (or even billions) of parameters. LLMs can be trained and then refined to perform several NLP tasks like generation of text, summarization, translation, prediction, and more. Challenges and Applications of Generative Large Language Models assists readers in understanding LLMs, their applications in various sectors, challenges that need to be encountered while developing them, open issues, and ethical concerns. LLMs are just one approach in the huge set of methodologies provided by AI. The book, describing strengths and weaknesses of such models, enables researchers and software developers to decide whether an LLM is the right choice for the problem they are trying to solve. AI is the new buzzword, in particular Generative AI for human language (LLMs). As such, an overwhelming amount of hype is obfuscating and giving a distorted view about AI in general, and LLMs in particular. Thus, trying to provide an objective description of LLMs is useful to any person (researcher, professional, student) who is starting to work with human language. The risk, otherwise, is to forget the whole set of methodologies developed by AI in the last decades, sticking with only one model which, although very powerful, has known weaknesses and risks. Given the high level of hype around such models, Challenges and Applications of Generative Large Language Models (LLMs) enables readers to clarify and understand their scope and limitations.

  • Provides a clear and objective description of LLMs, with their strengths and weaknesses
  • Demonstrates current applications of LLMs, along with strengths and known issues in each application
  • Covers not only the advantages but also risks that LLMs bring today, enabling readers to understand whether a particular LLM fits the problem at hand

Editorial Reviews

Review

A comprehensive reference that encompasses not just the benefits but also the potential risks associated with Large Language Models (LLMs) today, empowering readers to evaluate whether an LLM is suitable for their specific challenges

From the Back Cover

Large Language Models (LLMs) are a form of generative AI, based on Deep Learning, that rely on very large textual datasets, and are composed of hundreds of millions (or even billions) of parameters. LLMs can be trained and then refined to perform several NLP tasks like generation of text, summarization, translation, prediction, and more. Challenges and Applications of Generative Large Language Models assists readers in understanding LLMs, their applications in various sectors, challenges that need to be encountered while developing them, open issues, and ethical concerns. LLMs are just one approach in the huge set of methodologies provided by AI. The book, describing strengths and weaknesses of such models, enables researchers and software developers to decide whether an LLM is the right choice for the problem they are trying to solve. AI is the new buzzword, in particular Generative AI for human language (LLMs). As such, an overwhelming amount of hype is obfuscating and giving a distorted view about AI in general, and LLMs in particular. Thus, trying to provide an objective description of LLMs is useful to any person (researcher, professional, student) who is starting to work with human language. The risk, otherwise, is to forget the whole set of methodologies developed by AI in the last decades, sticking with only one model which, although very powerful, has known weaknesses and risks. Given the high level of hype around such models, Challenges and Applications of Generative Large Language Models (LLMs) enables readers to clarify and understand their scope and limitations.

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