Discussion with Reviewer 91WR (2/2)
##### [D2] Extension to large-scale networks
> We understand the reviewer's remaining concern, which seems to arise from two main aspects: the **(I) computation cost** and **(II) performance** of the proposed method on larger-scale networks. We would like to humbly clarify the following.
> - **(I)** The computational cost of our method on larger-scale networks
> - Above all, we humbly clarify again that the primary motivation for model editing is to enable data-efficient updates, not necessarily to reduce computational cost. *If full fine-tuning offers the best generalization-locality trade-off, it should indeed remain the preferred approach*.
> - When editing ViT/B-16, our method **demonstrates greater efficiency compared to some SOTA baselines**: ours consumes 50% FLOPs of full fine-tuning while KN consumes 77%.
> - When **editing SwinV2-g (1B)** which has 1134% more parameters than ViT/B-16 (86 MB), the FLOPs of our method only **marginally increases** by 435% (from 26.4G to 141.5 G), while the FLOPs of full fine-tuning increases by 976 % (from 52.8 G to 567.9 G).
> - **(II)** The performance of our method on larger-scale networks
> The reviewer's concern seems to stem from the following hypotheses: (1) larger networks require more FFNs to edit, (2) more FFNs to edit would necessitate significantly larger hypernetworks, and (3) the relatively small performance improvement in Fig r1 (b-c) is due to an in sufficiently large hypernetwork and, consequently, not enough FFNs being edited. However, we humbly clarify each point below.
> - **Larger networks do not necessarily require more FFNs to edit.**
> - We focus on localizing and editing **a visual concept**; the neurons that describe a visual concept are (1) **concentrated in the middle to late layers** [3], as the early layers encode distributed representations of very primitive concepts (e.g., geometric patterns) [1]; and (2) **compact** -- the number of neurons associated with a single concept does not largely increase as the model size grows [2], with the additional model capacity likely benefiting the representation of more diverse concepts.
> - During the discussion period, we conducted experiments by increasing the number of FFNs edited in SwinV2-g. The results, showing only marginal improvement when editing more FFNs, also corroborate that editing just 3 FFNs is sufficient to balance generalization and locality.
>
> |Number of FFNs |Identified layers by natrual 933-923 group |Generalization|Locality|
> |----|---|---|---|
> |3|19~21|94.77|75.95|
> |5|18~22|94.39|77.69|
> |8|17~24|95.92|75.24|
> |Number of FFNs|Identified layers by AI oil painting|Generalization|Locality|
> |3|19~21|87.99|72.53|
> |5|19~23|91.30|72.36|
> |8|17~24|88.18|71.23|
> - Our practice of editing 3 FFNs has also been validated successful and scalable in the context of LLMs by the prior hypernetwork-based method MEND [4], which successfully edits 3 FFNs across various scales of LLMs, including distilGPT-2 (82M), GPT-Neo (2.7B) and GPT-J (6B).
> - **Increasing the number of FFNs to edit requires only a marginal increase in the size of the hypernetwork.**
> - To accommodate larger channel sizes, the changes in the hypernetwork are limited to the input and output layers: (1) adding an input projection layer (e.g., 3584x768 for SwinV2-g) that projects the encoded image features to a lower dimension (which is the input dimension of the hypernetwork) and (2) increasing the output dimension of the output projection layer. For editing SwinV2-g, the size increase resulting from these two changes amounts to 29.3% of the original size of the hypernetwork.
> - To accommodate more FFNs for editing, the only change required in the hypernetwork is the addition of one more trainable input token for each FC layer.
> - **The relatively small performance improvement in Fig. r1(b-c) is attributed to the difference between SwinV2-g and ViT across various classes.**
> - We observed significant differences in the error samples produced by SwinV2-g and ViT for each class, reflecting distinct model behaviors.
> - The two groups reported in Fig. r1(b-c) were selected because our method for editing ViT showed the largest improvements (by up to 18% GR at the similar TRR) over the baselines (including FT-L2) on these groups. Although the improvement for SwinV2-g on these specific groups appears modest, during the discussion period, we found that our method for editing SwinV2-g achieved a 16% GR improvement at the similar TRR on other groups, including Vase in scene light dataset, 407-654 in the natural dataset.
[1] Identifying interpretable subspaces in image representations, ICML 2023.
[2] Labeling neural representations with inverse recognition, NeurIPS 2023.
[3] Interpreting CLIP's image representation via text-based decomposition, ICLR 2024.
[4] Fast model editing at scale, ICLR 2022.