Equation 6 · How Mechanistic Interpretability Research Is Actually Done
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Sparse dictionary learning is the field’s answer, and it is a second and different act of extraction rather than a departure from the first: a sparse autoencoder is trained on the very same activation vectors the probe was reading, learning an overcomplete basis in which each vector is reconstructed as a sparse combination of dictionary elements. Writing a for the activation, z for its sparse code and for the reconstruction,
Sources cited in the article section
- [6] Sparse Autoencoders Find Highly Interpretable Features in Language Models ↗
- [7] Towards Monosemanticity: Decomposing Language Models With Dictionary Learning ↗
- [8] Scaling and evaluating sparse autoencoders ↗
- [9] Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders ↗
- [10] Interpreting Attention Layer Outputs with Sparse Autoencoders ↗
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