Large Language Models

Concepts, Techniques and Applications

John Atkinson-Abutridy

• Publisher: CRC Press
• Year: 2024
• Print ISBN: 9781032836089
• eBook ISBN: 9781040134306
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Computer Vision & Pattern Recognition
• Index: Technology & Engineering

This book serves as an introduction to the science and applications of Large Language Models (LLMs). You'll discover the common thread that drives some of the most revolutionary recent applications of artificial intelligence (AI): from conversational systems like ChatGPT or BARD, to machine translation, summary generation, question answering, and much more.

At the heart of these innovative applications is a powerful and rapidly evolving discipline, natural language processing (NLP). For more than 60 years, research in this science has been focused on enabling machines to efficiently understand and generate human language. The secrets behind these technological advances lie in LLMs, whose power lies in their ability to capture complex patterns and learn contextual representations of language. How do these LLMs work? What are the available models and how are they evaluated? This book will help you answer these and many other questions. With a technical but accessible introduction:

• You will explore the fascinating world of LLMs, from its foundations to its most powerful applications
• You will learn how to build your own simple applications with some of the LLMs

Designed to guide you step by step, with six chapters combining theory and practice, along with exercises in Python on the Colab platform, you will master the secrets of LLMs and their application in NLP.

From deep neural networks and attention mechanisms, to the most relevant LLMs such as BERT, GPT-4, LLaMA, Palm-2 and Falcon, this book guides you through the most important achievements in NLP. Not only will you learn the benchmarks used to evaluate the capabilities of these models, but you will also gain the skill to create your own NLP applications. It will be of great value to professionals, researchers and students within AI, data science and beyond.

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Dedication
• Table of Contents
• Preface
• Introduction
• Author Biography
• Chapter 1 ◾ Introduction
• Chapter 2 ◾ Fundamentals
• Chapter 3 ◾ Large Language Models
• Chapter 4 ◾ Model Evaluation
• Chapter 5 ◾ Applications
• Chapter 6 ◾ Issues and Perspectives
• Bibliography
• Index