Understanding Generative Pre-trained Transformers

Aug 7, 2024

Lecture 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: Models learn from massive data sets, allowing for fine-tuning on specific tasks.
  • Transformer: Key invention in AI; a type of neural network.

Types of Transformer Models

  • Different models can process audio, text, images, etc.
  • Early transformer models focused on language translation (2017, Google).
  • Current models, like ChatGPT, are designed to predict subsequent text given context.
    • Generates a probability distribution of possible next words.

Text Generation Process

  1. Input Processing:
    • Text input is divided into tokens (words, character combinations).
    • Each token is linked to a vector representing its meaning.
  2. Attention Mechanism:
    • Tokens interact through an attention block to update meaning based on context.
  3. Feedforward Layer:
    • Vectors are processed in parallel to further refine meanings.
  4. Repetition:
    • Alternation between attention blocks and feedforward layers until final outputs are generated.
  5. Output Generation:
    • The last vector is processed to produce a probability distribution over potential next tokens.

Key Components

  • Tokens: Basic units of input (words, patches, etc.).
  • Vectors: Mathematical representations of tokens; similar meanings have nearby vectors in high-dimensional space.
  • Attention Block: Determines relevant context and updates word meanings.
  • Feedforward Layer (Multilayer Perceptron): Updates vectors using a standard operation in deep learning.

Prediction Model

  • A prediction model is generated from input data, allowing for text generation by:
    • Providing initial text (seed).
    • Sampling from the predicted distribution to append new text iteratively.

Training and Parameters

  • Deep Learning: Uses data to adjust model parameters instead of explicit programming.
  • Backpropagation: Common training algorithm for scaling deep learning models.
  • Parameters: Model weights (e.g., GPT-3 has 175 billion parameters).
  • Weight Matrices: Organize the parameters for processing input data through multiple layers.

Embeddings and Vectors

  • Embedding Matrix: Converts tokens into vectors, with each word represented in high-dimensional space (GPT-3 has 12,288 dimensions).
  • Semantic Meaning: Directions in embedding space carry meaning; similar words cluster together.
  • Example: Vector relationships can reveal linguistic relationships (e.g., king - man + woman = queen).

Context Size and Limitations

  • Transformers handle a fixed context size; GPT-3 uses a context size of 2048.
  • Longer inputs can lead to loss of context in conversation.

Output Layer and Softmax Function

  • Probability Distribution: Involves mapping the last vector to vocabulary probabilities.
  • Softmax: Normalizes outputs into a probability distribution; adjusts for predicted next tokens.
  • Logits: Raw unnormalized outputs before applying softmax; inputs to softmax are referred to as logits.

Summary

  • A good understanding of word embeddings, softmax, and matrix multiplications is key to grasping the attention mechanism.
  • Future chapters will delve deeper into the attention mechanism and other foundational concepts in deep learning.

Next Steps

  • The next chapter will focus on the attention blocks, critical for Transformer functionality.