Research map / Architectures and efficiency / Attention and transformers
TRANSFORMER EXPLAINER: Interactive Learning of Text-Generative Models
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Presents Transformer Explainer, an open-source, browser-based interactive visualization tool that runs a live GPT-2 model so non-experts can learn Transformer structure and math operations.
Problem
Transformers are opaque to non-experts; existing resources emphasize math intricacies or target practitioners' neuron/layer-level interpretability rather than accessible, interactive learning.
Approach
A web-based interactive tool that runs GPT-2 live in the browser and integrates a model overview with smooth transitions between low-level math operations and high-level model structure, using a Sankey-style flow visualization and a temperature slider to show next-token prediction.
Key results
- The open-source tool has amassed over 125,000 users since release.
- Runs a live GPT-2 model locally in the browser via ONNX runtime, with Svelte and D3 front end.
- Temperature slider lets users see how low vs high temperature sharpens or smooths the next-token distribution.
Limitations
- No user studies yet to assess efficacy and usability (planned future work).
- Inference speed and model size not yet optimized.
Experimental setup
Metrics: number of users (125,000)
Baselines: Blog posts (Alammar 2018) Karpathy video tutorial 3D Transformer visualization (Bycroft 2023)
Builds on: CNN Explainer ActiVis Talaria
Closest papers by meaning
- Understanding Transformers via N-Gram Statistics (NeurIPS 2024 · similarity 0.87)
- Trainable Transformer in Transformer (ICML 2024 · similarity 0.86)
- TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters (ICLR 2025 · similarity 0.84)
- Bigram Subnetworks: Mapping to Next Tokens in Transformer Language Models (NeurIPS 2025 · similarity 0.84)
- Towards Understanding Transformers in Learning Random Walks (NeurIPS 2025 · similarity 0.84)
- Algorithmic Capabilities of Random Transformers (NeurIPS 2024 · similarity 0.84)
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