Transformer Explainer: Interactive Learning of Text-Generative Models
Authors: name: Aeree Cho, name: Grace C. Kim, name: Alexander Karpekov, name: Alec Helbling, name: Zijie J. Wang, name: Seongmin Lee, name: Benjamin Hoover, name: Duen Horng Chau

Transformers have revolutionized machine learning, yet their inner workings
remain opaque to many. We present Transformer Explainer, an interactive
visualization tool designed for non-experts to learn about Transformers through
the GPT-2 model. Our tool helps users understand complex Transformer concepts
by integrating a model overview and enabling smooth transitions across
abstraction levels of mathematical operations and model structures. It runs a
live GPT-2 instance locally in the user's browser, empowering users to
experiment with their own input and observe in real-time how the internal
components and parameters of the Transformer work together to predict the next
tokens. Our tool requires no installation or special hardware, broadening the
public's education access to modern generative AI techniques. Our open-sourced
tool is available at https://poloclub.github.io/transformer-explainer/. A video
demo is available at https://youtu.be/ECR4oAwocjs.