AI Mini Series: Intro to LLM and Generative AI Titelbild

AI Mini Series: Intro to LLM and Generative AI

AI Mini Series: Intro to LLM and Generative AI

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 This course material introduces large language models (LLMs), focusing on the transformer architecture that powers them. It explains how LLMs work, including tokenization, embedding, and self-attention mechanisms, and explores various LLM applications in natural language processing. The text also covers prompt engineering techniques, such as zero-shot, one-shot, and few-shot learning, to improve model performance. Finally, it outlines a project lifecycle for developing and deploying LLM-powered applications, emphasizing model selection, fine-tuning, and deployment optimization. Briefing Document: Introduction to Large Language Models and Generative AI 1. Overview & Introduction to Generative AI Core Concept: Generative AI uses machine learning models that learn statistical patterns from massive datasets of human-generated content to create outputs that mimic human abilities.Focus: This course primarily focuses on Large Language Models (LLMs) and their application in natural language generation, although generative AI exists for other modalities like images, video, and audio.Foundation Models: LLMs are "foundation models" trained on trillions of words using substantial compute power, exhibiting "emergent properties beyond language alone" such as reasoning and problem-solving.Model Size: The size of a model, measured by its parameters (think of these as "memory"), directly correlates with its sophistication and ability to handle complex tasks. “And the more parameters a model has, the more memory, and as it turns out, the more sophisticated the tasks it can perform.”Customization: LLMs can be used directly or fine-tuned for specific tasks, allowing for customized solutions without full model retraining. 2. Interacting with Large Language Models Natural Language Interface: Unlike traditional programming, LLMs interact using natural language instructions.Prompts: The text input provided to an LLM is called a "prompt".Context Window: The "context window" is the memory space available for the prompt, typically a few thousand words, but varies by model.Inference & Completions: The process of using the model to generate text is called "inference." The model's output is called a "completion," comprising the original prompt and the generated text. “The output of the model is called a completion, and the act of using the model to generate text is known as inference. The completion is comprised of the text contained in the original prompt, followed by the generated text.” 3. Capabilities of Large Language Models Beyond Chatbots: LLMs are not just for chatbots but can perform diverse tasks, driven by the base concept of "next word prediction."Variety of Tasks: The text details capabilities including:Essay writingText SummarizationTranslation (including between natural language and machine code)Information Retrieval (e.g., named entity recognition)Augmented interaction via connection to external data and APIs.Scale & Understanding: Increased model scale (number of parameters) leads to improved subjective understanding of language, which is essential for processing, reasoning, and task-solving. "Developers have discovered that as the scale of foundation models grows from hundreds of millions of parameters to billions, even hundreds of billions, the subjective understanding of language that a model possesses also increases." 4. The Transformer Architecture & Self-Attention RNN Limitations: Previous models used Recurrent Neural Networks (RNNs), which were limited by computational resources and memory requirements, hindering their ability to capture long-range context. "RNNs while powerful for their time, were limited by the amount of compute and memory needed to perform well at generative tasks."Transformer Revolution: The 2017 "Attention is All You Need" paper introduced the Transformer architecture, which uses an "entirely attention-based mechanism". "In 2017, after the publication of this paper, Attention is All You Need, from Google and the University of Toronto, everything changed. The transformer architecture had arrived."Key Advantages: The transformer architecture allows for efficient scaling, parallel processing of input data, and the ability to "pay attention to the mean," leading to dramatically improved performance in natural language tasks.Self-Attention: The transformer's power stems from self-attention, which allows models to learn the relevance and context of all words in a sentence, not just adjacent words, by learning "attention weights" between each word. "The power of the transformer architecture lies in its ability to learn the relevance and context of all of the words in a sentence...not just to each word next to its neighbor, but to every other word in a sentence."Attention Maps: These visualize the relationships, highlighting word connections and their relevance within the sentence.Multi-Headed Self-Attention: The architecture learns multiple sets of self-attention weights in parallel...
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