Can I summarize your concerns as something like “I’m not sure that looking into the behavior of “real” models on narrow distributions is any better research than just training a small toy model on that narrow distribution and interpreting it?” Or perhaps you think it’s slightly better, but not considerably?
Between the two, I might actually prefer training a toy model on a narrow distribution! But it depends a lot on exactly how the analysis is done and what lessons one wants to draw from it.
Real language models seem to make extensive use of superposition. I expect there to be lots of circuits superimposed with the one you’re studying, and I worry that studying it on a narrow distribution may give a misleading impression – as soon as you move to a broader distribution, overlapping features and circuits which you previously missed may activate, and your understanding may in fact be misleading.
On the other hand, for a model just trained on a toy task, I think your understanding is likely closer to the truth of what’s going on in that model. If you’re studying it over the whole training distribution, features either aren’t in superposition (there’s so much free capacity in most of these models this seem possible!) or else they’ll be part of the unexplained loss, in your language. So choosing to use a toy model is just a question of what that model teaches you about real models (for example, you’ve kind of side-stepped superposition, and it’s also unclear to what extent the features and circuits in a toy model represent the larger model). But it seems much clearer what is true, and it also seems much clearer that these limitations exist.
Between the two, I might actually prefer training a toy model on a narrow distribution! But it depends a lot on exactly how the analysis is done and what lessons one wants to draw from it.
Real language models seem to make extensive use of superposition. I expect there to be lots of circuits superimposed with the one you’re studying, and I worry that studying it on a narrow distribution may give a misleading impression – as soon as you move to a broader distribution, overlapping features and circuits which you previously missed may activate, and your understanding may in fact be misleading.
On the other hand, for a model just trained on a toy task, I think your understanding is likely closer to the truth of what’s going on in that model. If you’re studying it over the whole training distribution, features either aren’t in superposition (there’s so much free capacity in most of these models this seem possible!) or else they’ll be part of the unexplained loss, in your language. So choosing to use a toy model is just a question of what that model teaches you about real models (for example, you’ve kind of side-stepped superposition, and it’s also unclear to what extent the features and circuits in a toy model represent the larger model). But it seems much clearer what is true, and it also seems much clearer that these limitations exist.