Equation 12 · How Mechanistic Interpretability Research Is Actually Done
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The penalty has a known cost: it does not directly control how many dictionary elements fire, only how much their combined magnitude is discouraged, and it systematically shrinks the elements that do fire toward zero, biasing the reconstruction. Two later refinements address this more directly. Gao and colleagues introduced k -sparse encoding, which drops the tunable penalty in favour of a fixed sparsity budget enforced structurally,
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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