This is an interesting line of research but missing a key aspect: there's (almost) no references to the linear representation hypothesis. Much work on neural network interpretability lately has shown individual neurons are polysemantic, and therefore practically useless for explainability. My hypothesis is fitting linear probes (or a sparse autoencoder) would reveal linearly semantic attributes.

It is unfortunate because they briefly mention Neel Nanda's Othello experiments, but not the wide array of experiments like the NeurIPS Oral "Linear Representation Hypothesis in Language Models" or even golden gate Claude.