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Georgia Tech Team Releases Transformer Explainer Tool to Demystify AI Language Models

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"Understanding that an LLM predicts token probability distributions helps users avoid taking outputs as absolute truth."
— Aeree Cho, Ph.D. student at Georgia Tech

Inside the Black Box: A New Tool Lets Anyone See How ChatGPT Thinks

Researchers at Georgia Tech have unveiled Transformer Explainer, a free, browser-based tool that pulls back the curtain on large language models (LLMs) like ChatGPT and Claude. Designed for non-experts, the interactive platform visualizes the complex inner workings of transformer architectures—the neural network foundation behind many of today's generative AI systems.

From Concept to Global Reach

Transformer Explainer requires no installation, running entirely within any web browser. Its impact has been swift and significant: within just three months of release, the tool attracted 150,000 users. As of the latest report, over 563,000 people worldwide have engaged with it.

The project will be formally presented at the 2026 Conference on Human Factors in Computing Systems (CHI 2026), scheduled for April 13-17 in Barcelona. This acceptance follows a best poster award earned at the 2024 IEEE Visualization Conference.

Addressing a Core Misconception

The tool directly tackles a widespread problem: the tendency to anthropomorphize LLMs. "Understanding that an LLM predicts token probability distributions helps users avoid taking outputs as absolute truth," explains Aeree Cho, a Ph.D. student involved in the project.

By demystifying the technology, Transformer Explainer helps users maintain a critical perspective, recognizing AI outputs as statistically generated predictions rather than sentient reasoning.

A Hands-On Visual Experience

Transformer Explainer employs live, interactive visualization to show text flowing through a model in real-time. Users can:

  • Input their own text and observe the model’s live next-word predictions
  • Navigate Sankey-style diagrams that trace how information moves through embeddings, attention heads, and transformer blocks
  • Toggle between high-level concepts and detailed mathematical operations
  • Adjust temperature settings to see how randomness directly alters predictions

This layered approach ensures that both curious beginners and technically-minded learners can find a comfortable entry point.

The Team Behind the Tool

Transformer Explainer was developed by a dedicated team of Georgia Tech researchers: Ph.D. students Aeree Cho, Alex Karpekov, and Grace Kim, with support from Alec Helbling, Seongmin Lee, Ben Hoover, and alumni Zijie (Jay) Wang and Minsuk Kahng. The project was supervised by Professor Polo Chau from the School of Computational Science and Engineering.