INTERP ATLAS

IA-009 / 2024

Transformer Explainer is showing , about .

ID
IA-009
Name
Transformer Explainer
URL
https://poloclub.github.io/transformer-explainer/
Artefact type
Interactive tool
Visual form
Makes visible
Role of the visual
Explanation
Runnable by a visitor
On your own inputs
Date published
2024-08-08
Year
2024
Link status
Live
Built by (people)
Aeree Cho; Grace C. Kim; Alexander Karpekov; Alec Helbling; Zijie J. Wang; Seongmin Lee; Benjamin Hoover; Duen Horng (Polo) Chau
Organisation
Georgia Institute of Technology (Polo Club of Data Science)
Venue / published in
IEEE VIS 2024; arXiv
Sector
Academia
Country / region
US
Open source
Yes
Code repo
https://github.com/poloclub/transformer-explainer
Method / technique
Live in-browser execution of a model with every stage of the forward pass visualised, and smooth transitions between abstraction levels
Model(s) studied
GPT-2
Access required
Open weights
What it visualises
The full GPT-2 forward pass — embeddings, attention queries keys and values, MLP, and next-token probabilities — updating live as you type
Interaction affordances
Type your own input and watch predictions update in real time; expand any stage down to the underlying matrix operations; adjust temperature; no installation or GPU required
Pragmatic vs Basic science
Neither / educational
Reverse-eng vs Concept-based
Neither
Observational vs Interventional
Observational
Intended audience
General public; Students
Description (card)
An interactive tool that runs a live GPT-2 instance in your browser and visualises every stage of the forward pass — embeddings, attention, MLP, output probabilities — updating as you type your own text.
Why it matters
The most accessible route into transformer internals that exists: no installation, no GPU, no prior knowledge. That is a different kind of contribution from any research result in this ledger, and arguably a wider-reaching one.
Visual / design notes
The core design idea is smooth movement between abstraction levels — you can zoom from a block diagram down to the matrix multiplication without losing your place.
Tags
Citation
Cho, A., Kim, G.C., Karpekov, A., Helbling, A., Wang, Z.J., Lee, S., Hoover, B. and Chau, D.H., 2024. Transformer Explainer: Interactive Learning of Text-Generative Models. IEEE VIS 2024. arXiv:2408.04619
Related entries
IA-010
Confidence
High
Source of info
Read the arXiv abstract page in full, 2026-08-06. The live tool itself was not inspected — it is client-rendered and the fetch returned an empty page shell.
Date added
2026-08-06
Added by
Claude
Notes
Educational rather than a research discovery: the architecture it visualises is known, not uncovered. A boundary case for the inclusion rule, logged because the visualisation IS the contribution and it was peer-reviewed at IEEE VIS. Access classified Open weights: GPT-2 runs locally in the visitor's browser, no server or account needed.