You're reading from Mastering NLP from Foundations to LLMs Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python

Product type Paperback

Published in Apr 2024

Publisher Packt

ISBN-13 9781804619186

Length 340 pages

Edition 1st Edition

Languages

Python

Concepts

Deep Learning

Authors (2):

Meysam Ghaffari

Lior Gazit

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Table of Contents (14) Chapters

Preface

1. Chapter 1: Navigating the NLP Landscape: A Comprehensive Introduction

2. Chapter 2: Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP FREE CHAPTER

3. Chapter 3: Unleashing Machine Learning Potentials in Natural Language Processing

4. Chapter 4: Streamlining Text Preprocessing Techniques for Optimal NLP Performance

5. Chapter 5: Empowering Text Classification: Leveraging Traditional Machine Learning Techniques

6. Chapter 6: Text Classification Reimagined: Delving Deep into Deep Learning Language Models

7. Chapter 7: Demystifying Large Language Models: Theory, Design, and Langchain Implementation

8. Chapter 8: Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG

9. Chapter 9: Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs

10. Chapter 10: Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI

11. Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts

12. Index

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Motivations for developing and using LLMs

The motivation to develop and use LLMs arises from several factors related to the capabilities of these models, and the potential benefits they can bring in diverse applications. The following subsections detail a few of these key motivations.

Improved performance

LLMs, when trained with sufficient data, generally demonstrate better performance compared to smaller models. They are more capable of understanding context, identifying nuances, and generating coherent and contextually relevant responses. This performance gain applies to a wide range of tasks in NLP, including text classification, named entity recognition, sentiment analysis, machine translation, question answering, and text generation. As shown in Table 7.1, the performance of BERT – one of the first well-known LLMs – and GPT is compared to the previous models on the General Language Understanding Evaluation (GLUE) benchmark. The GLUE benchmark is a collection...