SAE Latent Unit
SAE enables feature disentanglement from Superposition
Responds to both abstract discussions and concrete examples
There are features representing more abstract properties of the input, might there also be more abstract, higher-level actions which trigger behaviors over the span of multiple tokens?
SAE Feature Notion
SAE Feature Steering
SAE Feature Universality
SAE Feature Structure
SAE Feature Distribution
SAE Feature Matching
SAE Feature Splitting
SAE Feature Visualization
SAE Feature Shrinkage
SAE Feature Absorption
SAE pathological error
SAE Bounce Plot
SAE feature importance
SAE Feature Direction
SAE Feature Stitching
SAE Feature Circuit
SAE Feature Metrics
Insane visualization of feature activation distribution with conditional distribution and log-likelihood
What is Feature: the simplest factorization of the activations (SAE MDL)
As investigation of superposition has progressed, it's become clear that we don't really know what a "feature" is, despite them being central to our research agenda. Given a given a sparse factorization of the activations , let’s start by defining “total information” by fitting a probability distribution to the entries of the matrices and computing its entropy. (Larger dictionaries tend to require more information to represent, but sparser codes require less information to represent, which may counterbalance.) It turns out that measuring the "Total Information” seems to be an effective tool for determining the "correct number of features”, at least for synthetic data. Anthropic observes that dictionary learning solutions "bounce" when the dictionary size matches the true number of factors. This is also where the best MMCS score. If such bounces could be found in real data, it would seem like significant evidence that there are "real features" to be found.
High Activation Density could mean either that sparsity was not properly learned, or that it is an important feature needed in various situations. In the Feature Browser, SAE features show higher feature interpretability when they have more high activation Quantile, which demonstrates a limitation where SAE features have low interpretability for low activations and exhibit certain skewness.
However, features with the highest Activation Density in the Activation Distribution are less interpretable, mainly because these features typically don't have high activation values in absolute terms (not quantile). A well-classified and highly interpretable SAE feature should not show density that simply decreases with activation value, but rather should show clustering at high activation levels after an initial decrease.
Automated Interpretability Metrics proposal from EleutherAI
In the residual stream, features exhibit an antipodal pairing phenomenon where they are linearly paired with positive and negative values. Mathematically, if we allow negative values instead of just positive ones, we might be able to reduce the dictionary size. This antipodal pairing appears to be a simple pattern resulting from the current training structure that only uses positive values, rather than being a special feature phenomenon.