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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 laid out a comprehensive pathway for training LLMs, beginning with the imperative stage of data preparation and management. A robust corpus – varied, extensive, and balanced – is the bedrock upon which LLMs stand, requiring a diverse spectrum of text encompassing a broad scope of topics, cultural and linguistic representations, and temporal spans. To this end, we detailed the significance of collecting data that ensures a balanced representation and mitigates biases, hence fostering models that deliver a refined understanding of language.

Following the collection, rigorous processes of cleaning, tokenization, and annotation come into play to refine the quality and utility of data. These steps remove noise and standardize the text, breaking it into tokens that the model can efficiently process and annotate to provide contextual richness.

Data augmentation and preprocessing practices were emphasized as pivotal in expanding the scope of...

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