Regarding image models: our understanding is that strong regularization is required to split representations for MNIST autoencoding and CIFAR classification because there is a strong inductive bias towards learning features that are common to many classes of images. (In MNIST, 3s are similar to 8s, etc.; In CIFAR, similar edge detectors, etc. will be learned for many classes.) Basically, our learning target is highly unnatural. With our current experimental design, I don’t expect this to change with scale, so I’m less excited about investigating the effect of model or dataset size. That said, this dynamic might change if we explored examples with class imbalance (routing only a small fraction of classes and training on others as normal). I suspect this would reduce the need for regularization, leading to a reduction in alignment tax and perhaps more interesting dynamics with respect to scale. That’s an experiment we probably should have run (and still could, but we aren’t prioritizing image models right now).
As for localization for unlearning in language models, my personal take is that the idea is there but we don’t have the method quite right yet. I think there’s a reasonable chance (say, 40%) that we change our configuration a bit and are able to get localization much more stably, and with lower alignment tax both pre- and post-ablation. (If I understand correctly, my colleagues agree that this outcome is plausible but think it’s less likely than I do.) If we aren’t able to find this methodological improvement, then I don’t see a point in scaling. However, if we find it, then I expect scaling will be relatively cheap because, while we will still need to pre-train models, we won’t need to do any more hyperparameter tuning than is usual. Of course, whatever method we land on may turn out to have middling performance. In that case, to get a signal on whether this is worth doing, we may need to investigate a realistic unlearning setting, where the model and data are larger, and the forget set is a smaller portion of the training data.
In terms of improvements that we’re trying: we’re currently thinking about (a) insights we can borrow from mixture of experts models, and (b) about whether it is better to route only via edges leaving parameters rather than activations; the latter is what we currently do, and is far more aggressive.
I’m not sure if any of our ambitious alignment goals can be achieved via fine-tuning. Once the model has “settled on” certain ways of representing concepts, it seems too late to do the kinds of things we want.[1] But this may just be a lack of imagination! Given that PEFT can be viewed as a special case of gradient routing, maybe there’s something there.
We (led by Jacob) tried a variety of things to get Expand, Route, Ablate to work as a fine-tuning method for unlearning. Unsurprisingly, we weren’t able to get it to work.
Thanks for the thoughtful questions.
Regarding image models: our understanding is that strong regularization is required to split representations for MNIST autoencoding and CIFAR classification because there is a strong inductive bias towards learning features that are common to many classes of images. (In MNIST, 3s are similar to 8s, etc.; In CIFAR, similar edge detectors, etc. will be learned for many classes.) Basically, our learning target is highly unnatural. With our current experimental design, I don’t expect this to change with scale, so I’m less excited about investigating the effect of model or dataset size. That said, this dynamic might change if we explored examples with class imbalance (routing only a small fraction of classes and training on others as normal). I suspect this would reduce the need for regularization, leading to a reduction in alignment tax and perhaps more interesting dynamics with respect to scale. That’s an experiment we probably should have run (and still could, but we aren’t prioritizing image models right now).
As for localization for unlearning in language models, my personal take is that the idea is there but we don’t have the method quite right yet. I think there’s a reasonable chance (say, 40%) that we change our configuration a bit and are able to get localization much more stably, and with lower alignment tax both pre- and post-ablation. (If I understand correctly, my colleagues agree that this outcome is plausible but think it’s less likely than I do.) If we aren’t able to find this methodological improvement, then I don’t see a point in scaling. However, if we find it, then I expect scaling will be relatively cheap because, while we will still need to pre-train models, we won’t need to do any more hyperparameter tuning than is usual. Of course, whatever method we land on may turn out to have middling performance. In that case, to get a signal on whether this is worth doing, we may need to investigate a realistic unlearning setting, where the model and data are larger, and the forget set is a smaller portion of the training data.
In terms of improvements that we’re trying: we’re currently thinking about (a) insights we can borrow from mixture of experts models, and (b) about whether it is better to route only via edges leaving parameters rather than activations; the latter is what we currently do, and is far more aggressive.
I’m not sure if any of our ambitious alignment goals can be achieved via fine-tuning. Once the model has “settled on” certain ways of representing concepts, it seems too late to do the kinds of things we want.[1] But this may just be a lack of imagination! Given that PEFT can be viewed as a special case of gradient routing, maybe there’s something there.
We (led by Jacob) tried a variety of things to get Expand, Route, Ablate to work as a fine-tuning method for unlearning. Unsurprisingly, we weren’t able to get it to work.