Summary
This paper proposes a method called Neuron-level Sequential Editing (NSE) based on MEMIT to extend that method to the sequential model editing task. The main component is to always compute latent updates based on the original model weights, in order to avoid model failures during the sequential editing. They also conduct neuron-level update and iterative editing to further improve the performance. The comprehensive experiments show the superiority of their proposed method compared to editing methods that directly modify the model’s parameters.
Strengths
1. This paper focuses on the sequential editing task and the editing methods that modify the original model’s parameters. They extend the previous baseline MEMIT to this scenario and prove the superiority of their proposed method.
2. The authors have done comprehensive experiments to show the superiority of their method on the sequential model editing task compared to methods that modify the model’s parameters.
3. The authors release their code for reproducibility.
Weaknesses
1. The authors should make it clearer in their presentation that they focus on methods that modify the parameters. The models that freeze the model’s parameters, e.g., SERAC[1] and T-Patcher[2], show stable performance in sequential editing[3]. The authors don’t compare their method with these two strong baselines in the experiments. Therefore, it is more appropriate to clearly state this constraint in the Introduction, especially in the first paragraph. The first paragraph constrains the model editing methods into “Modify model’s parameters” methods (ROME and MEMIT), but neglect other methods that can also address this problem. In the following paragraphs, authors use *direct model editing* methods (which is not clear what this stands for) and *memory-based* methods to refer to *modify models’ parameters* and *preserve model’s parameters* methods (the taxonomy used in the Related Work section) respectively. It would be clearer and more coherent if using the taxonomy in the Related Work section across the entire paper.
2. The presentation of the mathematical notations should be improved. Details follow in the Questions section.
3. The main architecture figure 2 is misleading. $z_i$ is not a weight of the model, but a hidden state. The decoder blocks are not frozen since the W_out is updated.
4. It is not clear what “number of edits” means in the figures. To me, one edit means one edit to the model, i.e., one update to the model. However, it seems that “number of edits” refers to “number of edit samples” in this paper. In the previous papers, number of model edits and number of edit samples are proportional, since they only use once the edit samples. It’s not the case in the NSE as the authors adopt an iterative editing technique where one edit sample can be used to edit several times the model.
[1] Mitchell, Eric, et al. "Memory-based model editing at scale." International Conference on Machine Learning. PMLR, 2022.
[2] Huang, Zeyu, et al. "Transformer-Patcher: One Mistake Worth One Neuron." The Eleventh International Conference on Learning Representations. 2023
[3] Yao, Yunzhi, et al. "Editing Large Language Models: Problems, Methods, and Opportunities." Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
Questions
1. Issues related to mathematical notations.
1. In section 2.1, the vectors and matrices should be italic to be consistent with other sections.
2. In line 145, $n$ is not defined.
3. In line 150, $\Delta, W$ and $V_1$ are not bolded.
4. In line 219, it is not clear that the activation values are from a single layer or from all the layers to be modified. If it’s the activation values of a certain layer, it would be better to add the subscript of the layer $l$.
5. In line 220, Q should be italic. In addition, the absolute value of a vector is its norm. I think here the authors want to say the absolute value for each element in the vector. It would be clearer that the authors add some clarifications about what this absolute value sign means.
6. In equation 10, it is not appropriate to use the same symbol on both sides of the equation, one refers to a specific set while the other refers to a variable. It is thus not clear which set is referred to in the inequality.
7. In equations 3, 4, and 5, “edit” should be non-italic.
8. In Figure 2, the “in” and “out” for MLP layer weights should be non-italic, which will be consistent with the notation in line 230.
2. Does the batch size influence the edit success? I’m curious whether using different batch sizes will result in different number of model edits.
3. How probable is it that the method cannot edit successfully one sample and needs to do it iteratively?
4. In Figure 5, the degradation seems very sudden. Do the authors have any explanation why this degradation is sharp rather than gradual.
5. In line 431, what is the baseline model for the average improvement of 45.6%?
6. Although it seems out of the scope of this paper (depending on how the authors will address the weaknesses point 1), it would be interesting to compare the NSE method with T-Patcher and SERAC.