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Outline

Title
Abstract
Definition and Background
Advantages Over Previous Models
Introduction to Fine-Tuning
Introduction to Prompt Engineering
Prompt Design Patterns and Techniques
Applications and Case Studies
Introduction to Few-Shot and Zero-Shot Learning
Introduction to Chatbots with LLMS
Introduction to Retrieval-Augmented Generation
Introduction to Open Source LLMS
Llama: Design and Impact
Summary
Review Questions
References
All Topics
Computer Science
Natural Language Processing

Transformers and Large Language Models

Profile image of Saman SiadatiSaman Siadati

2023, Saman Siadati

https://doi.org/10.5281/ZENODO.15687613
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74 pages

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Abstract

The field of artificial intelligence (AI) is advancing rapidly, and at its core are transformative models that have redefined natural language processing: trans- formers and large language models (LLMs). This book, Transformers and Large Language Models, is written to help learners build a clear, practical understand- ing of the concepts, architectures, and techniques that drive these powerful systems. My journey into this field began over two decades ago with a degree in Applied Mathematics. I started my career as a statistical data analyst, even- tually moving into data mining and, later, data science. Along the way, I witnessed firsthand how crucial mathematical and computational foundations are—not only for understanding how these models work, but for applying them effectively to real-world problems.

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This paragraph of the first footnote will contain support information. Will fill in later. ABSTRACT Natural Language Processing (NLP) has become one of the leading application areas in the current Artificial Intelligence boom. Transfer learning has enabled large deep learning neural networks trained on the language modeling task to vastly improve performance in almost all language tasks. Interestingly, when the models are trained with data that includes software code, they demonstrate remarkable abilities in generating functioning computer code from natural language specifications. We argue that this creates a conundrum for claims that neural models provide an alternative theory to generative phrase structure grammars in explaining how language works. Since the acceptable syntax of programming languages is determined by phrase structure grammars, successful neural models are apparently uninformative about the theoretical foundations of programming languages, and by extension, natural languages. We argue that the term language model is misleading because deep learning models are not theoretical models of language and propose the adoption of corpus model instead, which better reflects the genesis and contents of the model. INDEX TERMS natural language processing, deep learning, syntax, linguistics, language model, automatic programming, neural networks 1 Chomsky describes his early work as an attempt to create a theoretical apparatus which was rich enough to describe the data. But it was always understood that the initial machinery had to be wrong because such a rich, complex system couldn't meet the criterion for biological evolution. The subsequent years were spent by reducing the complexity of the theoretical machinery https://youtu.be/pUWmTXkpHjE?t=3520 2 MIT150: Brains, Minds and Machines Symposium, June 16, 2011.

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References (30)

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