I don’t know! Seems hard It’s hard because you need to disentangle ‘interpretation power of method’ from ‘whether the model has anything that can be interpreted’, without any ground truth signal in the latter. Basically you need to be very confident that the interp method is good in order to make this claim. One way you might be able to demonstrate this, is if you trained / designed toy models that you knew had some underlying interpretable structure, and showed that your interpretation methods work there. But it seems hard to construct the toy models in a realistic way while also ensuring it has the structure you want - if we could do this we wouldn’t even need interpretability. Edit: Another method might be to show that models get more and more “uninterpretable” as you train them on more data. Ie define some metric of interpretability, like “ratio of monosemantic to polysemantic MLP neurons”, and measure this over the course of training history. This exact instantiation of the metric is probably bad but something like this could work