Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Mastering NLP from Foundations to LLMs

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

Arrow left icon
Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781804619186
Length 340 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Meysam Ghaffari Meysam Ghaffari
Author Profile Icon Meysam Ghaffari
Meysam Ghaffari
Lior Gazit Lior Gazit
Author Profile Icon Lior Gazit
Lior Gazit
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

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 13. Other Books You May Enjoy

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...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime