title: Transformer Explainer: Interactive Learning of Text-Generative Models
summary: 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 enabling smooth transitions across
  abstraction levels of mathematical operations and model structures. It runs a
  live GPT-2 instance 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. Our tool requires no installation or special hardware, broadening the
  public's education access to modern generative AI techniques. Our open-sourced
  tool is available at https://poloclub.github.io/transformer-explainer/. A video
  demo is available at https://youtu.be/ECR4oAwocjs.
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paper_authors.name: Alexander Karpekov
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paper_authors.fullname: Alex Karpekov
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paper_authors.fullname: Alec Helbling
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paper_authors.name: Zijie J. Wang
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paper_authors.name: Seongmin Lee
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paper_authors.fullname: Seongmin Lee
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paper_authors.name: Benjamin Hoover
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paper_authors.fullname: Ben Hoover
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paper_authors.name: Duen Horng Chau
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paper_authors.fullname: Duen Horng Chau
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paper_authors.user: polochau
paper_title: Transformer Explainer: Interactive Learning of Text-Generative Models
paper_summary: 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 enabling smooth transitions across
  abstraction levels of mathematical operations and model structures. It runs a
  live GPT-2 instance 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. Our tool requires no installation or special hardware, broadening the
  public's education access to modern generative AI techniques. Our open-sourced
  tool is available at https://poloclub.github.io/transformer-explainer/. A video
  demo is available at https://youtu.be/ECR4oAwocjs.
paper_ai_keywords: Transformers GPT-2 interactive visualization tool model overview abstraction levels mathematical operations model structures live GPT-2 instance internal components parameters generative AI