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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

Summary

In this chapter, we focused on the decision-making process of LLMs, which utilize a complex interplay of probabilistic modeling and statistical analysis to interpret and generate language. LLMs, such as GPT-4, are trained on extensive datasets, allowing them to predict the likelihood of word sequences within a given context. The Transformer architecture plays a crucial role in this process, with its attention mechanisms assessing different input text elements to produce relevant output. We further explored the nuances of LLM training, emphasizing the importance of context and patterns learned from data to refine the models’ predictive capabilities.

By addressing the challenges LLMs face, we provided insight into issues such as bias, ambiguity, and the balancing act between overfitting and underfitting. We also touched on the ethical implications of AI-generated content and the continuous need for model fine-tuning to achieve more sophisticated language understanding...

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