This page shows the most relevant public items for Mechanistic Interpretability, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Mar 10, 2020 • Distill • Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter
Neural networks are generally regarded as opaque black boxes. However, if we zoom in and carefully examine the weights and activations of convolutional neural networks, we find highly interpretable...
Paper • Mar 4, 2021 • Distill • Gabriel Goh, Nick Cammarata, Chelsea Voss, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, Chris Olah
We investigate the internal representations of the CLIP model and discover the presence of 'multimodal neurons'. These neurons fire not only for specific visual features (like a spider) but also fo...
Paper • May 9, 2023 • OpenAI • Steven Bills, Nick Cammarata, Dan Mossing, Henk Tillman, Leo Gao, Gabriel Goh, Ilya Sutskever, Jan Leike, Jeff Wu, William Saunders
Understanding the internal mechanisms of massive language models is a critical bottleneck for AI safety and alignment. Given the billions of parameters in modern models, manual human inspection of ...
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