# Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation

arXiv: https://arxiv.org/abs/2408.04619 · HTML: https://mlsanity.com/paper/2408.04619/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 93 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 26 of 26)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [Eliciting Latent Predictions from Transformers with the Tuned Lens](https://mlsanity.com/paper/2303.08112/) (2023)
- [Compressing Large-Scale Transformer-Based Models: A Case Study on BERT](https://mlsanity.com/paper/2002.11985/) (2020)
- [Information Flow Routes: Automatically Interpreting Language Models at Scale](https://mlsanity.com/paper/2403.00824/) (2024)
- [A Survey of Resource-efficient LLM and Multimodal Foundation Models](https://mlsanity.com/paper/2401.08092/) (2024)
- [CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization](https://mlsanity.com/paper/2004.15004/) (2020)
- [VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers](https://mlsanity.com/paper/2203.17247/) (2022)
- [AttentionViz: A Global View of Transformer Attention](https://mlsanity.com/paper/2305.03210/) (2023)
- [Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Models](https://mlsanity.com/paper/2009.07053/) (2020)
- [GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation](https://mlsanity.com/paper/1809.01587/) (2018)
- [Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems](https://mlsanity.com/paper/2109.15142/) (2021)
- [How Does Attention Work in Vision Transformers? A Visual Analytics Attempt](https://mlsanity.com/paper/2303.13731/) (2023)
- [Direct-Manipulation Visualization of Deep Networks](https://mlsanity.com/paper/1708.03788/) (2017)
- [Dodrio: Exploring Transformer Models with Interactive Visualization](https://mlsanity.com/paper/2103.14625/) (2021)
- [Visualizing and Understanding Patch Interactions in Vision Transformer](https://mlsanity.com/paper/2203.05922/) (2022)
- [VisQA: X-raying Vision and Language Reasoning in Transformers](https://mlsanity.com/paper/2104.00926/) (2021)
- [Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion](https://mlsanity.com/paper/2305.03509/) (2023)
- [Influence Patterns for Explaining Information Flow in BERT](https://mlsanity.com/paper/2011.00740/) (2020)
- [Understanding Multi-Head Attention in Abstractive Summarization](https://mlsanity.com/paper/1911.03898/) (2019)
- [A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness](https://mlsanity.com/paper/2301.13617/) (2023)
- [ManimML: Communicating Machine Learning Architectures with Animation](https://mlsanity.com/paper/2306.17108/) (2023)
- [Panda or not Panda? Understanding Adversarial Attacks with Interactive Visualization](https://mlsanity.com/paper/2311.13656/) (2023)

## Built on by (most cited first, 10 of 10)

- [Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods](https://mlsanity.com/paper/2508.12730/) (2025)
- [DS-Diffusion: Data Style-Guided Diffusion Model for Time-Series Generation](https://mlsanity.com/paper/2509.18584/) (2025)
- [ChannelExplorer: Exploring Class Separability Through Activation Channel Visualization](https://mlsanity.com/paper/2505.04647/) (2025)
- [NeuroBreak: Unveil Internal Jailbreak Mechanisms in Large Language Models](https://mlsanity.com/paper/2509.03985/) (2025)
- [ConceptViz: A Visual Analytics Approach for Exploring Concepts in Large Language Models](https://mlsanity.com/paper/2509.20376/) (2025)
- [VAE Explainer: Supplement Learning Variational Autoencoders with Interactive Visualization](https://mlsanity.com/paper/2409.09011/) (2024)
- [AnimatedLLM: Explaining LLMs with Interactive Visualizations](https://mlsanity.com/paper/2601.04213/) (2025)
- [ViT-Explainer: An Interactive Walkthrough of the Vision Transformer Pipeline](https://mlsanity.com/paper/2604.02182/) (2026)
- [RADIANT-LLM: an Agentic Retrieval Augmented Generation Framework for Reliable Decision Support in Safety-Critical Nuclear Engineering](https://mlsanity.com/paper/2604.22755/) (2026)
- [The Architect: Interactive Visualization of Deep Learning Mathematics Directly in Microsoft Excel](https://mlsanity.com/paper/2608.13572/) (2026)

## Abstract (arXiv)

The Transformer architecture underpins modern large language models powering state-of-the-art text generation and AI applications. However, its complexity makes it difficult for non-experts to learn. Existing resources often lack interactivity, rely on static descriptions of simplified architectures, or fail to reflect models' behavior with real data. To address this gap, we introduce Transformer Explainer, an interactive visualization tool for non-experts to learn Transformers. The tool integrates an overview illustrating the Transformer's data flow with on-demand explanations that gradually reveal mathematical details. Smooth transitions across abstraction levels highlight the interplay between high-level structures and low-level operations. Running a live GPT-2 instance directly in the browser, Transformer Explainer empowers learners to experiment with custom input and hyperparameters without setup, observing next-token predictions in real time. A 90-participant user study showed that our tool offered significant advantages in improving user understanding and engagement. Transformer Explainer has attracted over 490,000 users.