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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

Challenges and limitations in LLM decision-making

LLMs such as GPT-4 are technological marvels, but they come with a set of challenges and limitations that impact their decision-making abilities. Here are some of the challenges and limitations we must consider:

  • Understanding context and nuance:
    • Ambiguity: LLMs may struggle with ambiguity in language. They sometimes cannot determine the correct meaning of a word or phrase without clear context.
    • Sarcasm and irony: Detecting sarcasm or irony is particularly challenging because it often requires understanding subtle cues and having a deep cultural context that LLMs may not have.
    • Long-term context: Maintaining coherence over long conversations or documents is difficult as LLMs might lose track of earlier context.
  • Generalization versus specialization:
    • Overfitting: LLMs can become too specialized to the training data, making them less able to generalize to new types of data or problems
    • Underfitting: Conversely, LLMs might not capture...
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