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.