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Decoding Large Language Models

Decoding Large Language Models

By : Irena Cronin
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Decoding Large Language Models

Decoding Large Language Models

4 (3)
By: Irena Cronin

Overview of this book

Ever wondered how large language models (LLMs) work and how they're shaping the future of artificial intelligence? Written by a renowned author and AI, AR, and data expert, Decoding Large Language Models is a combination of deep technical insights and practical use cases that not only demystifies complex AI concepts, but also guides you through the implementation and optimization of LLMs for real-world applications. You’ll learn about the structure of LLMs, how they're developed, and how to utilize them in various ways. The chapters will help you explore strategies for improving these models and testing them to ensure effective deployment. Packed with real-life examples, this book covers ethical considerations, offering a balanced perspective on their societal impact. You’ll be able to leverage and fine-tune LLMs for optimal performance with the help of detailed explanations. You’ll also master techniques for training, deploying, and scaling models to be able to overcome complex data challenges with confidence and precision. This book will prepare you for future challenges in the ever-evolving fields of AI and NLP. By the end of this book, you’ll have gained a solid understanding of the architecture, development, applications, and ethical use of LLMs and be up to date with emerging trends, such as GPT-5.
Table of Contents (22 chapters)
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1
Part 1: The Foundations of Large Language Models (LLMs)
4
Part 2: Mastering LLM Development
9
Part 3: Deployment and Enhancing LLM Performance
14
Part 4: Issues, Practical Insights, and Preparing for the Future

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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