This is cool work! There are two directions I'd be excited to see explored in more detail:

1. You mention that you have to manually tune the value of the perturbation weight R. Do you have ideas for how to automatically determine an appropriate value? This would significantly increase the scalability of the technique. One could then easily generate thousands of unsupervised steering vectors and use a language model to flag any weird downstream behavior.
2. It is very exciting that you were able to uncover a backdoored behavior without prior knowledge. However, it seems difficult to know whether weird behavior from a model is the result of an intentional backdoor or whether it is just a strange OOD behavior. Do you have ideas for how you might determine if a given behavior is the result of a specific backdoor, or how you might find the trigger? I imagine you could do some sort of GCG-like attack to find a prompt which leads to activations similar to the steering vectors. You might also be able to use the steering vector to generate more diverse data on which to train a traditional dictionary with an SAE.

Thank you for catching this. 

These linked to section titles in our draft gdoc for this post. I have replaced them with mentions of the appropriate sections in this post.