Sparse Autoencoders2 articles

Sparse Autoencoders

Articles

Sparse Autoencoders (SAEs) and Mechanistic Interpretability: Mathematical Foundations, Dictionary Learning, Top-K Sparsity, Feature Steering, and Monosemanticity
Sparse Autoencoders (SAEs) and Mechanistic Interpretability: Mathematical Foundations, Dictionary Learning, Top-K Sparsity, Feature Steering, and Monosemanticity Modern autoregressive large language models represent a vast catalog of world concepts, syntactic rules, and abstract reasoning heuristics. However, inspecting the raw weight matrices and internal activation states of transformer networks reveals an obstinate barrier to mechanistic interpretability: individual neurons are notoriously p
August 29, 20261 min
Superposition and Sparse Autoencoders: Mathematical Foundations, the Polysemanticity Bottleneck, and Dictionary Learning in Language Models
For years, attempts to interpret transformer neural networks by inspecting individual neurons encountered an obstinate barrier: polysemanticity. A single neuron in an intermediate multi-layer perceptron (MLP) or residual stream layer rarely corresponds to a single human-interpretable concept. Instead, the same neuron frequently fires on a disparate mixture of inputs, such as Python syntax errors, discussions of Renaissance art, and Spanish verbs. This phenomenon prevents mechanistic interpretabi
August 25, 20261 min