How does ChatGPT work? Explained by Deep-Fake Ryan Gosling.

Introduction

Hello everyone! My name is Ryan Gosling, and I’m here to give you a quick introduction to the fascinating world of text-to-text capabilities of generative AI. Whether you're AI curious, AI unknowing, or just AI confused, I’ll break down the concepts for you.

What Are Large Language Models (LLMs)?

A large language model (LLM) is a type of artificial intelligence designed to understand and generate human language. These models can perform a variety of tasks, such as:

• Translating languages
• Composing text
• Answering questions
• Writing code
• Summarizing lengthy documents
• Generating creative content
• Providing explanations on complex topics
• Engaging in human-like conversations

Some well-known examples of LLMs include GPT-4 by OpenAI, Gemini by Google, Claude 3 by Anthropic, Mistral by Mistral AI, Llama by Meta, and Grok by X. Some of these models are open-source, like Mistral and Llama, allowing anyone to use, modify, and share them, while others are commercial and provide unique features for businesses.

How Do LLMs Work?

The process of generating text using LLMs involves several key steps that transform user input into AI-generated output. Let’s look at a simplified version of this process.

Input Prompt

When you provide a prompt to an LLM, such as, “Please give me a short speech of a Premier League football coach who wants to motivate his team at halftime when they are 0-2 behind,” the model begins by breaking the input down into smaller pieces called tokens. Tokens can be words, parts of words, or characters, depending on the model's design.

Tokenization

In this case, the input prompt is split into 33 tokens. Each token typically represents a word, although longer words may be split into several tokens.

Creating Initial Embeddings

The next step involves converting each token into an embedding, which is a numerical representation that captures the semantic properties of that token. For example, the token "motivate" might have an embedding where numbers represent attributes like:

• 0.95: Likelihood of the token being a verb
• 0.87: Emotional intensity of the word
• -0.45: Relation to the current performance level

These embeddings come from parameters established during the pre-training phase on vast amounts of text, including books and articles.

Context-Aware Embeddings

A revolutionary aspect of LLMs is the transformation of initial embeddings into context-aware embeddings using a self-attention mechanism. This allows the model to assess the importance of each word in the input prompt and their relationships to one another.

Decoding to Output

Once context-aware embeddings are generated, they are organized into a matrix that informs the model about the likely next outputs. The model generates output one token at a time, starting with the most probable token based on the matrix.

For example, the first output could be "listen," and it is then added back into the input prompt. The process continues iteratively, producing more tokens until the entire output is formed.

A Philosophical Analogy

We can compare this process to generating the story of your life; it begins with a specific set of inputs and continuously adds new moments based on context—similar to how LLMs generate new sentences based on prior context.

Conclusion

This breakdown illustrates the essential workings of an LLM like ChatGPT, where "G" stands for generative (it generates output), "P" stands for pre-trained (utilizes parameters of a pre-trained model), and "T" stands for Transformers (the architecture enabling these processes).

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Keywords

• Large Language Models (LLMs)
• Text-to-Text Generation
• Tokens
• Embeddings
• Context-Aware Embeddings
• Self-Attention Mechanism
• Transformer Architecture

FAQ

1. What is an LLM?

   • A Large Language Model (LLM) is an AI model designed to understand and generate human language.

2. What tasks can LLMs perform?

   • LLMs can translate languages, summarize texts, compose content, engage in conversation, and much more.

3. How does the input prompt work in LLMs?

   • The input prompt is broken down into smaller pieces called tokens, which are then transformed into embeddings for understanding.

4. What is the self-attention mechanism?

   • It's a process that allows the model to determine the relevance of different words in a given context, creating context-aware embeddings.

5. What does GPT stand for?

   • GPT stands for Generative Pre-trained Transformer.