

Highlight 1
The app offers a unique visualization of LLM processes by extracting circuits, making complex models interpretable for users.
Highlight 2
It provides concrete, example-driven insights into how grammatical features are constructed, enhancing the learning experience.
Highlight 3
The app actively encourages user feedback, fostering a community-driven approach to development and improvement.

Improvement 1
The user interface could be made more intuitive to enhance usability for users who may not be familiar with model architectures.
Improvement 2
Providing tutorials or documentation for beginners could help users better understand how to use the app effectively.
Improvement 3
Expanding the model capabilities beyond GPT-2 to include other architectures could attract a broader user base.
Product Functionality
Integrate additional model architectures beyond GPT-2 to widen the scope of analysis for users.
UI & UX
Consider a redesign of the interface to make it more user-friendly, especially for beginners unfamiliar with machine learning concepts.
SEO or Marketing
Enhance the website's SEO by implementing targeted keywords related to LLM interpretability and offering case studies or success stories.
MultiLanguage Support
Adding multi-language support could make the app accessible to a global audience, expanding its reach.
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What is the purpose of this app?
The app extracts interpretable circuits from models using the GPT-2 architecture, helping users understand how specific inputs affect token probabilities.
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Who can benefit from using this app?
Researchers, educators, and students interested in natural language processing and machine learning can benefit from the insights provided by the app.
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How can I provide feedback on the app?
You can reach out directly to the developer, Peter Lai, through the contact options available on the app's website.