Thank you for your response
> Dear Reviewer CqwV,
>
> Thank you for your time and clarifying your points. We deeply apologize for any misunderstanding that may have occurred. We truly value your insightful suggestion, as it provides us with an important opportunity to further clarify this matter. Below, we will address specific questions to ensure comprehensive understanding.
>**Q1:** What would happen if that layer was dropped entirely. It simply may be the case that clipping a layer at 100% (i.e. dropping the layer) may in fact be the **best option**.
>**A:** This is indeed a very meaningful question! You're absolutely right; dropping the layer **could be the best option specifically for optimizing generalization error**. Here's a detailed analysis:
>
>(1) In our theoretical framework, we model **each layer** of the Transformer do a **single iteration of implicit gradient descent** (ICL). This scientific analysis references [1, 2, 3].
>
>(2) In **Theorem 2**, **L**-layer Transformer:
>
>Expected generalization error = population risk ($L_{\mu}$) - empirical risk ($L_{H_s}$)
>
>Expected generalization error = $\sqrt{\frac{R^2}{N}\sum_{t=1}^Ld\log (\lVert\Delta W_t\rVert_F^2 \lVert G_t\rVert_F^2)}$ (show the main part). If you drop the entire layer, it will
>
>change from $\sum_{t=1}^L$ to $\sum_{t=1}^{L-1}$. Therefore, dropping the layer may in fact be the **best option for generalization error**.
>
>(3) However, according to the traditional statistical learning viewpoint, performance can be **bounded by the sum of optimization error and generalization error** (Please see global rebuttal for "How should Theorem 2 be interpreted?").
>
>Thus, during the pruning process, there is a **trade-off** between optimization and generalization. Therefore, as pruning increases, the model's performance tends to **first improve and then decline** (the provided reference's method does not harm but also does not improve), as demonstrated in the experiments shown in **Figure 1**.
>
>**In conclusion, your suggestion (dropping the entire layer) can be a potential method (best option for generalization error) in our theoretical framework, but it may not necessarily be the best option for model performance.**
>
>[1] Johannes von Oswald et al. Transformers learn in-context by gradient descent. ICML 2023.
>
>[2] Ekin Akyiurek et al. What learning algorithm is in-context learning? investigations with linear models. ICLR 2023.
>
>[3] Damai Dai et al. Why can gpt learn in-context? language models secretly perform gradient descent as meta-optimizers. ACL(Findings) 2023.
>**Q2:** What is the predefined clipping rate set which is mentioned in algorithm 1?
> **A:** Firstly, the details of the algorithm can be reviewed in the **code** provided. Specifically, the set of clipping rate candidates is predefined. In our study, the clipping rate candidates are set as shown in **Figures 1 and 2**: [0, 0.1, 0.5, 0.75, 0.9, 0.95, 0.99, 0.995]. Besides, we analyze the impact of different hyperparameters through comparative experiments (details in **Section 2** and **Section 3.4**), as detailed below (We will provide further explanations in subsequent versions).
>
> - Clipping rate $\xi$：We search for the optimal $\xi$ in the predefined clipping rate candidates.
>
> - Predefined module $\mathcal{O}$ :
>
> The module containing the target pruning weights, which can be chosen from
>
> ['k_proj', 'q_proj', 'v_proj', 'out_proj', 'fc_in', 'fc_out', 'all', 'mlp', 'attn']
>
> - Selected layer $l$ : The layer containing the target pruning weights. For examlpe: In **Section 2**, we mainly focuse on comparing the impact of weight pruning to the **first two and the last two layers** of the model.
>
> - ICL shot number : The demonstration number in ICL, we analyze the effect of different ICL shot numbers in **Section 2.2**.