TRANSFORMER EXPLAINER: Interactive Learning of Text-Generative Models
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 smooth transitions across abstraction levels of math operations and model structures. It runs a live GPT-2 model 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. 125,000 users have used our open-source tool at https://poloclub.github.io/ transformer-explainer/.
Authors
- Duen Horng Chau (ORCID: https://orcid.org/0000-0001-9824-3323)
- Alec Helbling
- Benjamin Hoover (ORCID: https://orcid.org/0000-0001-5218-3185)
- Grace Kim
- Zijie J. Wang (ORCID: https://orcid.org/0000-0003-4360-1423)
- Seongmin Lee (ORCID: https://orcid.org/0000-0002-1564-5077)
- Aeree Cho
- Alexander Karpekov
Institutions
- Georgia Institute of Technology (US)
- OpenAI (United States) (US)
Publication Details
- Journal
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2025-04-11
- DOI
- https://doi.org/10.1609/aaai.v39i28.35347
- Citations
- 6
- Primary Topic
- Natural Language Processing Techniques
- Type
- article
- Field-Weighted Citation Impact
- 5.32