Makes sense! Depends on if you're thinking about the values as "estimating zero ablation" or "estimating importance."

Very cool work! 

• In the attention attribution section, you use clean_pattern * clean_pattern_grad as an approximation of zero ablation; should this be -clean_pattern * clean_pattern_grad? Zero ablation's approximation is (0 - clean_pattern)*clean_pattern_grad = -clean_pattern * clean_pattern_grad.
  • Currently, negative name movers end up with negative attributions, but we'd like them to be positive (since zero ablating helps performance and moves our metric towards one), right?
  • Of course, this doesn't matter when you are just looking at magnitudes.
• Cool to note we can approximate mean ablation with (means - clean_act) * clean_grad_act!
• (Minor note: I think the notebook is missing a `model.set_use_split_qkv_input(True)`. I also had to remove `from transformer_lens.torchtyping_helper import T`.)