Lecture on ChatGPT and Transformer Models

Introduction to ChatGPT

• ChatGPT has gained significant attention in the AI community.
• It is a system that allows interaction with AI for text-based tasks.
• Example tasks: writing haikus, humorous prompts, explanations, etc.
• It operates as a language model, generating text sequentially based on input.
• ChatGPT is probabilistic; similar prompts can yield different outputs.
• It models language by predicting word sequences.

Underlying Technology

• ChatGPT relies on the Transformer architecture.
• Originates from 2017 paper "Attention is All You Need."
• Transformers have become a fundamental part of AI applications.
• GPT stands for Generatively Pre-trained Transformer.

Implementing a Language Model

• Goal: Build a Transformer-based language model.
• Example dataset: Tiny Shakespeare (concatenated works of Shakespeare).
• Focus: Character-level language modeling.

Preprocessing and Tokenization

• Convert text into sequences of integers (tokenization).
• Use character-level tokenization for simplicity.
• Vocabulary size determined by unique characters in data.
• Split data into training and validation sets.

Training the Model

• Train on random data chunks due to computational constraints.
• Each chunk is sampled with a context length (block size).
• Batch processing to utilize GPU capabilities efficiently.
• Simple neural network: Bigram language model
  • Evaluates loss using cross-entropy.
  • Generates text by predicting following characters.

Implementing a Transformer

• Incorporate self-attention: nodes communicate with past tokens.
• Use queries, keys, and values for attention mechanism.
• Affinities determined by dot-product of keys and queries.
• Implement multi-head attention for parallel communication channels.
• Add feedforward networks for token-level computation.

Optimizing the Transformer

• Introduce residual connections and layer normalization.
• Residual connections aid gradient flow and optimization.
• Layer Norm stabilizes activations by normalizing features.
• Incorporate Dropout for regularization.

Training a Larger Model

• Scale up model with more layers, heads, and larger embedding dimensions.
• Use Dropout for improved regularization in large models.
• Achieve better validation loss and more coherent text generation.

ChatGPT Training Process

• Pre-training: Train on a large corpus (e.g., internet text) for document completion.
• Fine-tuning: Align model to behave as an assistant.
  • Collect supervised data with questions and answers.
  • Train reward model for preferred responses.
  • Use policy optimization to improve response quality.

Conclusion

• Successfully implemented a character-level Transformer model.
• Covered principles behind Transformer architecture and training.
• Discussed broader context of ChatGPT and its training pipeline.
• Future work: Fine-tuning and aligning models for specific tasks.

Resources: nanogpt repository for implementing small-scale GPT models.