Notes on Generative Pre-trained Transformers (GPT)

Introduction to GPT

• GPT: Stands for Generative Pre-trained Transformer.
• Generative: Bots that generate new text.
• Pre-trained: Model learns from massive datasets and can be fine-tuned for specific tasks.
• Transformer: Core invention in AI; a type of neural network.

Purpose of the Lecture

• Visually explain how transformers work.
• Follow the data flow inside a transformer step by step.

Types of Transformer Models

• Different models can produce various outputs:
  • Audio to transcript.
  • Text to synthetic speech.
  • Text to image (e.g., DALL-E, Midjourney).
• Original transformer (2017 by Google) was developed for language translation.
• Focus on predictive models that generate text based on input.

Predicting Text

• Prediction Process:
  • Takes input text, possibly with images/sound, predicts next piece of text.
  • Involves generating a probability distribution of possible next tokens.
  • Use cases include tools like ChatGPT.

Generating Longer Text

• Start with initial text, then sample from the distribution to append new text.
• Example: GPT-2 vs. GPT-3 in generating coherent stories.

Data Flow in Transformers

Tokenization

• Input broken into tokens (words, pieces of words, or characters).
• Each token is associated with a vector (numerical representation).

Attention Mechanism

• Attention Block: Allows vectors to communicate and update each other.
  • Focuses on context relevance for updating meanings of words based on proximity in meaning.

Feedforward Layer

• Vectors processed in parallel through a multilayer perceptron (MLP).
• Each vector undergoes the same operation.

Matrix Multiplication

• Core operations involve matrix multiplication of weights with data.
• Weights determine behavior and are learned during training.

Repeating Process

• Cycle between attention blocks and feedforward layers.
• Final vector encodes comprehensive meaning and is used for predictions.

Output Generation

• Use last vector to create a probability distribution of next tokens using an unembedding matrix.
• Softmax Function: Converts outputs into a valid probability distribution.

Training and Context Size

• Context Size: Determines how much text can be considered at once (e.g., GPT-3 with a context size of 2048).
• Important for maintaining conversation flow in chatbots.

Conclusion & Next Steps

• Next chapter will dive deeper into the attention mechanism and other details.
• Importance of understanding foundational concepts like embeddings, softmax, and matrix operations before exploring attention.

Key Concepts to Remember

• Weight Matrices: Parameters that learn during training.
• Tokens: Basic units of input (words, characters).
• Embeddings: High-dimensional representations of tokens.
• Dot Product: Measures alignment of vectors in embedding space.
• Softmax and Temperature: Control probability distributions for next token generation.