Transformer Explainer: Interactive Learning of Text-Generative Models

Aeree Cho, Grace Kim, Alex Karpekov, Alec Helbling, Zijie J. Wang, Seongmin Lee, Ben Hoover, Duen Horng Chau

Transformer Explainer: Interactive Learning of Text-Generative Models: 175 upvotes on Hugging Face Daily Papers, #1 of 13 papers on 2024-08-09. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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