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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Multiple agents – forming a team of LLMs that collaborate

This section deals with one of the most exciting recent methods in the world of LLMs, employing multiple LLMs simultaneously. In the context of this section, we seek to define multiple agents, each backed by an LLM and given a different designated role to play. Instead of the user working directly with the LLM, as we see in ChatGPT, here, the user sets up multiple LLMs and sets their role by defining a different system prompt for each of them.

Potential advantages of multiple LLM agents working simultaneously

Much like with people working together, here too, we see the advantages of employing several LLMs simultaneously.

Some advantages are the following:

Enhancing validation and reducing hallucinations: It has been shown that when providing feedback to an LLM and asking it to reason or to check its response, the reliability of its response improves. When designating roles for the various LLM agents on...