SHAP would be absurdly expensive to do for even tiny models (naive SHAP scales exponentially in the number of parameters; you can sample your coalitions to do better but those samples are going to be ridiculously sparse when you're talking about billions of parameters) and provides very little explanatory power for deep neural nets.

SHAP basically does point by point ablation across all possible subsets, which really doesn't make sense for LLMs. This is simultaneously too specific and too general.

It's too specific because interesting LLM behavior often requires talking about what ensembles of neurons do (e.g. "circuits" if you're of the mechanistic interpretability bent), and SHAP's parameter-by-parameter approach is completely incapable of explaining this. This is exacerbated by the other that not all neurons are "semantically equal" in a deep network. Neurons in the deeper layers often do qualitatively different things than earlier layers and the ways they compose can completely confuse SHAP.

It's too general because parameters often play many roles at once (one specific hypothesis here is the superposition hypothesis) and so you need some way of splitting up a single parameter into interpretable parts that SHAP doesn't do.

I don't know the specifics of what this particular model's approach is.

But SHAP unfortunately does not work for LLMs at all.

Completely agree with all your points!

Here is what this model does: it `rewrites` the model's activations (during pre-training) into supervised + unsupervised concepts that are then decoded into tokens. So at pre-training, we constrained the model with 33k supervised concepts (e.g., sports, toxicity, alignment, demographic variables), and then have more (101k) unsupervised concepts for the model to learn as well.

Overall, the architecture and loss functions of this model allow you to answer the following questions: 1) Which token in the context caused a chunk (group of tokens) to be generated? 2) which high level concept (supervised or unsupervised) caused the 3) perhaps more interestingly, in a single forward pass, we can tell you which training chunk led to the output of the model as well.

We do all of this for the single steerling model which is 8B parameters trained on 1.5T tokens. First time any model of this scale has achieved this level of interpretability by design.

would be happy to answer more questions.

Note that the parameters to SHAP can be things other than the model parameters (e.g. model inputs), it's very not obvious what those should be. Indeed that's often the central problem for interpretability (what are my actual features) and SHAP is entirely silent on what those features should be. SHAP could work as a final step if you have a small feature set. But I doubt that LLMs will have a small set of features for any reasonable interpretation of what they do.