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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

Human-in-the-loop – incorporating human judgment in evaluation

HITL is a concept where human judgment is used in conjunction with AI systems to improve the overall decision-making process. This integration of human oversight into the evaluation phase is particularly important for complex systems such as LLMs, where nuanced understanding and context may be required. Let’s take a closer look at HITL in the context of LLM evaluation:

  • Enhanced decision-making: Humans can provide nuanced assessments that go beyond what can be measured through automated metrics alone. This is especially critical for subjective areas such as language subtleties, cultural context, and emotional tone.
  • Quality control: Involving humans in the evaluation process can help maintain high quality and accuracy in the model’s outputs. Humans can catch errors or biases that automated tests might miss.
  • Training data refinement: Human evaluators can help refine training data by providing...
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