Transformer Explainer: Interactive Learning of Text-Generative Models
This tool addresses the need for accessible education on generative AI for the general public, though it is incremental as it builds on existing visualization and educational methods.
The authors tackled the problem of making Transformer models more understandable by developing Transformer Explainer, an interactive visualization tool that runs GPT-2 in a browser to help non-experts learn about its inner workings through real-time experimentation.
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.