- In the “Updating Facts” section, although the results seem to show that modifying the neurons using the word embeddings is effective, the paper lacks a discussion on this. It is not intuitive to me that there is a connection between a neuron at a middle layer and the word embeddings (which are used at the input layer). - Using integrated gradients to measure the attribution has been studied in existing papers. The paper also proposes post-processing steps to filter out the “false-positive” neurons, however, the paper doesn’t show how important these post-processing steps are. I think an ablation study may be needed.
- The paper lacks details of experimental settings. For example, how are those hyperparameters ($t$, $p$, $\lambda_1$, etc.) tuned? In table 5, why do “other relations” have a very different scale of perplexity compared to “erased relation” before erasing? Are “other relations” randomly selected?
- The baseline method (i.e., using activation values as the attribution score) is widely used in previous studies. Although the paper empirically shows that the baseline is not as effective as the proposed method, - I expect more discussion on why using activation values is not a good idea.
- One limitation of this study is that the paper only focuses on single-word cloze queries (as discussed in the paper).
- Figure 3: The illustration is not clear to me. Why are there two “40%” in the figure?
- I was confused that the paper targets single-token cloze queries or multi-token ones. I did not see a clear clarification until reading the conclusion.
- Using integrated gradients to measure the attribution has been studied in existing papers. The paper also proposes post-processing steps to filter out the “false-positive” neurons, however, the paper doesn’t show how important these post-processing steps are. I think an ablation study may be needed.