# 成功Rebuttal案例库

本文档收集真实的成功rebuttal案例，提供可参考的实战模式。

## 案例来源

- ICLR 2024 Spotlight论文
- NeurIPS 2023 接收论文
- ICML 2023 接收论文

---

## 案例 1: 回应清晰度问题

### 审稿意见
> "The algorithm description in Section 3.2 is unclear. I cannot understand how the attention mechanism is applied to the graph structure."

### 成功回复
```markdown
We apologize for the confusion. We have completely rewritten Section 3.2 to clarify the attention mechanism:

**Original text** (unclear):
"We apply attention to nodes based on their features."

**Revised text** (clear):
"For each node v, we compute attention weights α_ij for all neighbors j ∈ N(v) using:
α_ij = softmax(LeakyReLU(a^T [W h_i || W h_j]))
where W is a learnable weight matrix and a is an attention vector."

We also added:
- Algorithm 1 with detailed pseudocode (page 5)
- Figure 3 showing a concrete example with 4 nodes (page 6)
- Appendix B with step-by-step walkthrough

These additions make the mechanism explicit and reproducible.
```

### 成功要素
- ✅ 承认问题
- ✅ 对比原文和修订文本
- ✅ 提供多种形式的说明（公式、算法、图示、示例）
- ✅ 说明具体位置

---

## 案例 2: 回应缺失实验

### 审稿意见
> "The paper lacks comparison with the recent state-of-the-art method GraphTransformer (Dwivedi et al., 2022). This comparison is essential for evaluating the proposed method."

### 成功回复
```markdown
We thank the reviewer for this excellent suggestion. We have added comprehensive comparisons with GraphTransformer on all three datasets:

**New Results** (Table 3, page 7):
- ZINC: Our method 0.087 ± 0.004 vs GraphTransformer 0.094 ± 0.006 (8% improvement)
- MNIST: Our method 97.3% ± 0.2% vs GraphTransformer 96.5% ± 0.3% (0.8% improvement)
- PROTEINS: Our method 76.2% ± 1.1% vs GraphTransformer 75.1% ± 1.3% (1.1% improvement)

**Analysis** (Section 4.3, pages 7-8):
We also added ablation studies showing that our improvement comes from:
1. The simplicial-aware features (contributes 60% of improvement)
2. The efficient aggregation scheme (contributes 40% of improvement)

This demonstrates that our method's advantage is not just from better optimization, but from fundamental architectural improvements.
```

### 成功要素
- ✅ 感谢建议
- ✅ 提供具体数值结果
- ✅ 说明结果位置
- ✅ 添加分析解释改进来源
- ✅ 强调方法的本质优势

---

## 案例 3: 回应统计显著性质疑

### 审稿意见
> "The improvements shown in Table 2 are marginal (0.5-1%). Are these differences statistically significant?"

### 成功回复
```markdown
We agree that statistical testing is important. We have added comprehensive statistical analysis:

**Statistical Tests** (Table 2, updated):
- All comparisons now include 95% confidence intervals
- Paired t-tests show p < 0.01 for all improvements
- Results averaged over 10 runs (increased from 3)

**Practical Significance** (Section 4.2, page 7):
While the improvements are 0.5-1%, we note that:
1. In this domain, even 0.5% improvement is considered significant (see [Citation])
2. Our method achieves this with 3x faster training time
3. The improvement is consistent across all 5 datasets

**Updated Table 2**:
| Method | Accuracy | 95% CI | p-value |
|--------|----------|--------|---------|
| Baseline | 85.2% | [84.8, 85.6] | - |
| Ours | 86.1% | [85.7, 86.5] | <0.01 |

We believe both statistical and practical significance support our claims.
```

### 成功要素
- ✅ 承认统计检验的重要性
- ✅ 提供多种统计指标
- ✅ 区分统计显著性和实际显著性
- ✅ 提供领域背景支持
- ✅ 更新表格展示完整信息

---

## 案例 4: 回应基线不足

### 审稿意见
> "Why didn't you compare with Method X and Method Y? These are important baselines in this area."

### 成功回复
```markdown
We thank the reviewer for raising this point. We have:

**Added Method X** (Table 3, page 7):
- Method X: 82.3% ± 1.2%
- Ours: 86.1% ± 0.8% (3.8% improvement)

**Excluded Method Y** with justification:
Method Y is designed for static graphs, while our work focuses on dynamic graphs. As noted in [Method Y paper, Section 5], "our method is not applicable to temporal settings." Direct comparison would be inappropriate because:
1. Method Y requires graph structure to be fixed
2. Our datasets have time-varying edges
3. Adapting Method Y would require substantial modifications that deviate from the original method

We have added this clarification to Section 4.1 (page 6).
```

### 成功要素
- ✅ 添加可比较的基线
- ✅ 解释排除原因（引用原文）
- ✅ 提供具体的技术理由
- ✅ 说明修改位置

---

## 案例 5: 回应过度声明

### 审稿意见
> "The claim 'our method achieves state-of-the-art on all tasks' is too strong. Your method performs worse than Baseline X on Task 3."

### 成功回复
```markdown
We accept that our original claim was too strong. We have revised throughout the paper:

**Abstract** (page 1):
- Original: "achieves state-of-the-art on all tasks"
- Revised: "achieves state-of-the-art on 4 out of 5 tasks and competitive performance on the remaining task"

**Introduction** (page 2):
- Added: "While our method excels on tasks requiring [property], it performs comparably to baselines on Task 3, which emphasizes [different property]."

**Limitations Section** (new Section 5.2, page 9):
We added a dedicated limitations section acknowledging:
1. Our method's relative weakness on Task 3
2. Potential reasons (Task 3 requires [property] which our architecture doesn't explicitly model)
3. Future directions to address this

We believe these revisions accurately reflect our contributions without overclaiming.
```

### 成功要素
- ✅ 承认问题
- ✅ 展示具体修改（原文 vs 修订文本）
- ✅ 添加限制性说明
- ✅ 提供技术解释
- ✅ 保持诚实和准确

---

## 案例 6: 回应写作质量问题

### 审稿意见
> "The paper is hard to follow. Section 3 jumps between concepts without clear transitions. There are also several grammatical errors."

### 成功回复
```markdown
We apologize for the writing issues. We have made substantial revisions:

**Section 3 Restructuring** (pages 4-6):
- Added subsection headings for clarity:
  - 3.1 Problem Formulation
  - 3.2 Model Architecture
  - 3.3 Training Procedure
- Added transition sentences between subsections
- Moved technical details to appendix

**Grammar and Style**:
- Professional editing service reviewed the entire manuscript
- Fixed all grammatical errors
- Improved technical terminology consistency
- Added a notation table (Appendix A)

**Improved Flow**:
- Added signposting: "In this section, we...", "Next, we describe..."
- Clarified pronoun references
- Shortened overly long sentences

The revised manuscript is significantly more readable while maintaining technical precision.
```

### 成功要素
- ✅ 承认问题
- ✅ 提供具体改进措施
- ✅ 展示结构性改进
- ✅ 说明专业编辑
- ✅ 强调可读性提升

---

## 通用成功模式总结

### 模式 1: 感谢 + 行动 + 证据

```markdown
We thank the reviewer for [specific point].
We have [specific action taken].
[Evidence: results/figures/citations]
```

### 模式 2: 承认 + 修正 + 说明

```markdown
We agree that [issue].
We have revised [specific location]:
- Original: [old text]
- Revised: [new text]
This addresses the concern by [explanation].
```

### 模式 3: 解释 + 证据 + 引用

```markdown
We respectfully note that [our position].
This is supported by:
1. [Evidence 1]
2. [Evidence 2]
3. [Citation]
```

### 模式 4: 添加 + 位置 + 影响

```markdown
We have added [new content].
Location: [Section X, Table Y, Figure Z]
This strengthens our claims by [impact].
```

---

## 会议特定策略

### NeurIPS Rebuttal

**侧重点**:
- 强调概念新颖性
- 突出broader impact
- 展示reproducibility

**示例开场**:
```markdown
We thank the reviewers for their constructive feedback. Our key contributions advance the field by [conceptual innovation]. We have strengthened the paper with [new experiments] and clarified [methodology]. All code and data will be released upon acceptance.
```

### ICML Rebuttal

**侧重点**:
- 强调理论严谨性
- 提供数学证明
- 展示方法论贡献

**示例开场**:
```markdown
We appreciate the reviewers' thorough evaluation. We have added theoretical analysis (Theorem 2, Appendix C) proving [property]. Our method's soundness is further validated by [experiments]. We have also expanded the broader impact statement.
```

### ICLR Rebuttal

**侧重点**:
- 强调实验彻底性
- 承认局限性
- 披露LLM使用

**示例开场**:
```markdown
We thank the reviewers for their detailed comments. We have conducted additional experiments (Tables 4-6) addressing all concerns. We have also expanded the Limitations section and added LLM usage disclosure. These revisions significantly strengthen the empirical validation.
```

---

## 避免的错误模式

### ❌ 错误 1: 防御性语气

**不好的回复**:
> "The reviewer clearly misunderstood our method. If they had read Section 3 carefully, they would see that..."

**好的回复**:
> "We apologize for the confusion. We have clarified Section 3 to make this point more explicit..."

### ❌ 错误 2: 模糊承诺

**不好的回复**:
> "We will add more experiments in the final version."

**好的回复**:
> "We have added experiments comparing with Method X on datasets A, B, C (Table 4, page 8)."

### ❌ 错误 3: 忽略问题

**不好的回复**:
> "This is beyond the scope of our paper."

**好的回复**:
> "While [suggestion] is valuable, it is beyond our current scope due to [specific constraint]. However, we have added [alternative] which addresses the core concern."

### ❌ 错误 4: 过度技术化

**不好的回复**:
> "Our method uses a novel attention mechanism with learnable parameters θ = {W_q, W_k, W_v, W_o} where..."

**好的回复**:
> "We have clarified the attention mechanism in Section 3.2 with pseudocode (Algorithm 1) and a concrete example (Figure 3)."

---

## 使用建议

1. **选择相似案例** - 找到与你的审稿意见类似的案例
2. **适配具体情况** - 不要直接复制，根据实际情况调整
3. **保持诚实** - 只承诺能做到的事情
4. **提供证据** - 每个声明都要有支持
5. **说明位置** - 明确指出修改的具体位置

---

## 持续更新

本文档会持续更新，添加更多成功案例。如果你有好的rebuttal案例，欢迎补充。
