paper
referenced-only
2023
paper:bricken-towards-monosemanticity-decomposing-lang-2023Towards monosemanticity: Decomposing language models with dictionary learning
Feature splittingDisentanglementActivation Interval Sampling8 Billion MLP Activation SamplesFinite State Automata Feature AssembliesSparse Autoencoder for Dictionary LearningAttribution SimilarityThe PileLinear representationFeature Interpretability RubricMechanistic InterpretabilityMasked Cosine SimilarityNeuron Resampling
Methods (5)
- Activation Interval SamplingDividing feature activation spectrum into 11 evenly-spaced intervals and sampling uniformly to evaluate monosemanticity across activation levels
- Attribution SimilarityCorrelating attribution vectors (feature activation × logit weight of next token) across model pairs to measure functional universality
- Feature Interpretability Rubric14-point scoring rubric for human evaluation of feature interpretability covering confidence, activation consistency, logit consistency, and specificity
- Masked Cosine SimilarityCosine similarity between feature activations restricted to tokens where one of the features fires; used to identify feature splitting relationships
- Neuron ResamplingPeriodically reinitializing dead autoencoder neurons using high-loss data points to improve feature coverage
Frameworks (2)
- DisentanglementRelated research agenda seeking representations that separate conceptually distinct factors; contrasted with superposition approach
- Sparse Autoencoder for Dictionary LearningPrimary method introduced: trains a one-hidden-layer MLP with L1 sparsity penalty to decompose model activations into overcomplete feature dictionaries
Datasets (2)
- 8 Billion MLP Activation SamplesDataset of transformer MLP activations used to train sparse autoencoders; collected from 40M contexts
- The PileTraining corpus used for the 67M-parameter model tested with VPD.
Related work— refs + corpus + external arXiv
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