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Decoding Large Language Models

You're reading from   Decoding Large Language Models An exhaustive guide to understanding, implementing, and optimizing LLMs for NLP applications

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781835084656
Length 396 pages
Edition 1st Edition
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Author (1):
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Irena Cronin Irena Cronin
Author Profile Icon Irena Cronin
Irena Cronin
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Table of Contents (22) Chapters Close

Preface 1. Part 1: The Foundations of Large Language Models (LLMs)
2. Chapter 1: LLM Architecture FREE CHAPTER 3. Chapter 2: How LLMs Make Decisions 4. Part 2: Mastering LLM Development
5. Chapter 3: The Mechanics of Training LLMs 6. Chapter 4: Advanced Training Strategies 7. Chapter 5: Fine-Tuning LLMs for Specific Applications 8. Chapter 6: Testing and Evaluating LLMs 9. Part 3: Deployment and Enhancing LLM Performance
10. Chapter 7: Deploying LLMs in Production 11. Chapter 8: Strategies for Integrating LLMs 12. Chapter 9: Optimization Techniques for Performance 13. Chapter 10: Advanced Optimization and Efficiency 14. Part 4: Issues, Practical Insights, and Preparing for the Future
15. Chapter 11: LLM Vulnerabilities, Biases, and Legal Implications 16. Chapter 12: Case Studies – Business Applications and ROI 17. Chapter 13: The Ecosystem of LLM Tools and Frameworks 18. Chapter 14: Preparing for GPT-5 and Beyond 19. Chapter 15: Conclusion and Looking Forward 20. Index 21. Other Books You May Enjoy

Setting up rigorous testing protocols

Setting up rigorous testing protocols is crucial for evaluating the effectiveness and reliability of LLMs. These protocols are designed to thoroughly assess the model’s performance and ensure it meets the required standards before deployment. The following sections will provide a detailed exploration of how to set up such protocols.

Defining test cases

Defining test cases is a systematic approach to verifying that an LLM behaves as expected. Let’s take a closer look at what goes into this process:

  • Typical cases: These are scenarios that the model is expected to encounter frequently. For an LLM, typical cases might involve common phrases or questions that it should be able to understand and respond to accurately. The purpose is to confirm that the model performs well under normal operating conditions.
  • Boundary cases: These are situations that lie at the edge of the model’s operational parameters. For LLMs,...
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