Autoencoder

NLAs are not trained to target the workspace specifically, so their text may describe any content recoverable from the activation.

Sparse Autoencoders decomposes an activation into a sparse combination of learned dictionary features.

An activation is read out as the descriptions of its most strongly active features.

Like NLAs, SAEs are not trained to target the workspace specifically and readout quality depends on how accurate the feature labels are.