Lecture on Visualization using Transformers

Introduction

• The lecture focuses on creating code for visualization.
• Emphasis on teaching a class about Transformers, the technology behind models like ChatGPT.
• Aim to visualize the self-attention mechanism with interactive components.

Transformers and Self-Attention

• Transformers model the relationships between words in a sequence.
• Self-attention is used to understand these relationships.
• Visualization of self-attention can enhance understanding.

Using a New Model for Visualization

• Attempting to use a new model (o1 preview) to aid in visualization.
• Unlike previous models (e.g., GPT-40), this model "thinks" before outputting.

Requirements for Visualization

• Example sentence: "The quick brown fox."
• When hovering over a token, visualize edges with thickness proportional to attention scores.
  • Thicker edges indicate more relevance between words.

Challenges with Existing Models

• Existing models may miss instructions if too many are given at once.
• The new model's slower, careful reasoning reduces the chance of missing instructions.

Code Implementation and Testing

• Code output was copy-pasted into a terminal using the D editor of 2024 (Vim HTML).
• Visualization tested in a browser:
  • Hovering displays arrows indicating attention scores.
  • Clicking shows detailed attention scores.
  • Minor rendering issues (e.g., overlapping) noted.

Conclusion

• The new model performed well, creating a visualization better than could be done manually.
• Potential for use in creating visualization tools for teaching sessions.