Comments (2)
Hey @Framartin,
The gradnorm attack is under-construction (should have mentioned it somewhere- my bad!). We started working on it thinking it would be a nice addition, so pasted some placeholder code and docstrings (hence the mixup).
Gradients with respect to input tokens would indeed require modification- a good solution could be to fix the docstring for now and add the other as a TODO (we can pick up later when we get the time, but you're more than welcome to submit a PR if you want).
Gradient-norm attacks can be tricky for the very reason you mentioned; apart from this behavior (one may work better than other) the choice of parameters (e.g. which layer's parameters to use) could also have some impact. Perhaps a simple addition strategy (to take gradient norms for both weights and input tokens) could help?
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Fixed the docstring and closing this issue for now. We might add a token-based gradient attack in a future version, but please feel free to submit a PR in the meanwhile if you have a working implementation!
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