✅ 逐點回覆每個意見
✅ 明確標示修改位置（頁數、行數）
✅ 感謝審稿人的建議
✅ 快速完成修改並重新提交

✅ 優先處理核心問題
✅ 若需補充實驗，說明實驗設計
✅ 若無法完全滿足，提供替代方案
✅ 保持與編輯的溝通

✅ 仔細評估是否值得重投
✅ 若重投，需大幅改寫
✅ 在 Cover Letter 中說明重大改進
✅ 考慮是否轉投其他期刊

Dear Dr. [Editor's Name],

Thank you for the opportunity to revise our manuscript titled "[Your Title]" (Manuscript ID: [ID]). We appreciate the constructive feedback from the reviewers and have carefully addressed all comments. Below, we provide a point-by-point response to each reviewer's comments.

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**REVIEWER #1**

**Comment #1:** [Original comment]

**Response:** [Your detailed response]

**Changes made:** [Specific changes with page/line numbers]

---

**Comment #2:** [Original comment]

**Response:** [Your detailed response]

**Changes made:** [Specific changes with page/line numbers]

---

**REVIEWER #2**

[Same structure...]

---

**Summary of Major Changes:**
1. [Major change 1]
2. [Major change 2]
3. [Major change 3]

We believe these revisions have significantly strengthened the manuscript and hope it is now suitable for publication in [Journal Name].

Sincerely,
[Your Name]
Corresponding Author

✅ 審稿人意見：藍色或斜體
✅ 您的回覆：黑色正體
✅ 修改內容：紅色或粗體
✅ 頁碼行號：括號標示

Thank you for bringing these important references to our attention. We have now incorporated the following studies into our manuscript:

1. [Citation 1] - Discussed in Introduction (Page 3, Lines 56-59)
2. [Citation 2] - Compared with our findings in Discussion (Page 18, Lines 367-372)
3. [Citation 3] - Added to support our methodology (Page 10, Line 198)

These additions have enriched our literature review and strengthened the contextualization of our findings.

We apologize for the confusion. We have revised Figure 3 and its accompanying text to improve clarity:

1. Redesigned the figure with clearer labels and legends (Page 16, Figure 3)
2. Added a more detailed caption explaining each panel (Page 16, Lines 312-318)
3. Revised the Results section to provide step-by-step interpretation (Page 15, Lines 298-305)
4. Added a schematic diagram to illustrate the underlying mechanism (Page 17, Figure 4)

We believe these changes make our findings more accessible and reduce the risk of misinterpretation.

We agree that a more comprehensive discussion of limitations is warranted. We have substantially expanded the Limitations section (Page 21, Lines 428-456) to address:

1. Sample size and generalizability concerns (Lines 430-436)
2. Potential selection bias in participant recruitment (Lines 437-442)
3. Lack of long-term follow-up data (Lines 443-448)
4. Reliance on self-reported measures (Lines 449-453)
5. Implications of these limitations for interpreting our findings (Lines 454-456)

We have also discussed how future studies could address these limitations (Page 22, Lines 457-465).

We note that Reviewers 1 and 2 have differing opinions regarding sample size adequacy. We have carefully considered both perspectives:

**In response to Reviewer 1's concern:**
We have conducted a post-hoc power analysis (Page 12, Lines 234-238) and added a sensitivity analysis (Page 15, Table 4) to demonstrate the robustness of our findings despite the modest sample size.

**In response to Reviewer 2's assessment:**
We agree that our sample size is comparable to similar studies in the field (e.g., [citations]). However, we have also acknowledged this as a limitation and proposed larger-scale validation studies in the Discussion (Page 21, Lines 440-445).

We believe this balanced approach addresses both reviewers' concerns while maintaining scientific rigor.

We appreciate the reviewer's suggestion for long-term validation. However, conducting a 5-year longitudinal study is beyond the scope of the current manuscript, which focuses on [specific aim]. 

As a compromise, we have:
1. Added a cross-sectional analysis of participants at different time points (Page 14, Table 5)
2. Included retrospective data spanning 2 years (Page 16, Figure 6)
3. Explicitly proposed a longitudinal study as future research in the Discussion (Page 22, Lines 468-475)

We believe these additions provide preliminary evidence of temporal patterns while acknowledging the need for dedicated longitudinal research.

We apologize for the confusion. Upon reviewing our manuscript, we realize that our wording in the Results section (original Page 15, Lines 289-292) may have been misleading. We did not intend to claim X; rather, our finding indicates Y.

To clarify:
1. We have revised the problematic sentence to read: "[New, clearer wording]" (Page 15, Lines 289-291)
2. Added a clarifying statement in the Discussion (Page 19, Lines 385-388)
3. Modified Figure 5's caption to better reflect the actual finding (Page 16, Lines 320-323)

We thank the reviewer for identifying this ambiguity, which has helped us improve the clarity of our manuscript.

✅ "Thank you for this insightful comment."
✅ "We appreciate the reviewer bringing this to our attention."
✅ "This is an excellent suggestion that has improved our manuscript."

✅ "We thank the reviewer for catching this error."
✅ "Upon re-examination, we agree that our original analysis was flawed."
✅ "We apologize for this oversight and have corrected it throughout the manuscript."

✅ 新增內容：紅色或藍色
✅ 修改內容：綠色
✅ 刪除內容：刪除線

Dear Dr. Smith,

Thank you for the opportunity to revise our manuscript titled "AI-Assisted Literature Review: A Systematic Approach" (Manuscript ID: JAIR-2024-1234). We are grateful for the constructive feedback from the reviewers, which has significantly improved our manuscript. Below, we provide a detailed point-by-point response to each comment.

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**REVIEWER #1**

**Comment #1:** The introduction lacks a clear statement of the research gap. Please clarify what specific problem your study addresses that has not been adequately covered in previous research.

**Response:** We thank the reviewer for this important observation. We have substantially revised the Introduction to explicitly articulate the research gap (Page 3, Lines 58-72). Specifically, we now highlight that while previous studies have explored AI applications in literature review (e.g., Zhang et al., 2023; Kumar et al., 2024), none have systematically evaluated the accuracy and efficiency trade-offs across different AI models in the context of systematic reviews. Our study fills this gap by providing a comprehensive comparative analysis of five leading AI models (GPT-4, Claude, Gemini, LLaMA, and Mistral) using a standardized evaluation framework.

**Changes made:**
- Added explicit research gap statement (Page 3, Lines 58-65)
- Clarified our unique contribution (Page 3, Lines 66-72)
- Added a comparison table of previous studies (Page 4, Table 1)

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**Comment #2:** The sample size (n=50 articles) seems small for drawing generalizable conclusions. Can the authors justify this choice or consider expanding the dataset?

**Response:** We appreciate this concern. Our sample size was determined through a power analysis (α=0.05, power=0.80, effect size d=0.6), which indicated that 45 articles would be sufficient to detect meaningful differences between AI models. However, in response to this comment, we have:

1. Expanded our dataset from 50 to 75 articles (Page 8, Line 156)
2. Ensured balanced representation across five research domains (Page 8, Table 2)
3. Conducted a sensitivity analysis to verify result stability (Page 15, Figure 4)
4. Added a post-hoc power analysis confirming 90% power with the expanded sample (Page 12, Lines 245-248)

The revised results (Page 14, Table 4) show consistent patterns with our original findings, suggesting robustness across sample sizes.

---

**REVIEWER #2**

**Comment #1:** The methodology section needs more detail on how the "accuracy" of AI-generated summaries was evaluated. What specific criteria were used?

**Response:** Thank you for highlighting this important gap. We have substantially expanded the Methods section to provide a detailed description of our evaluation framework (Page 10, Lines 198-225). Our accuracy assessment is based on four criteria:

1. **Factual Correctness** (40%): Alignment with original article content
2. **Completeness** (30%): Coverage of key findings and methods
3. **Coherence** (20%): Logical flow and clarity
4. **Citation Accuracy** (10%): Correct attribution of claims

Each criterion was scored independently by three expert raters (inter-rater reliability κ=0.82, Page 11, Line 228). We have also added the complete scoring rubric as Supplementary Material S1.

**Changes made:**
- Detailed evaluation criteria (Page 10, Lines 198-210)
- Rater training protocol (Page 10, Lines 211-218)
- Inter-rater reliability analysis (Page 11, Lines 226-230)
- Added scoring rubric (Supplementary Material S1)

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**Comment #2:** Figure 3 is difficult to interpret. Consider using a different visualization or adding more explanatory text.

**Response:** We agree that the original Figure 3 was overly complex. We have completely redesigned this figure to improve clarity:

1. Replaced the 3D scatter plot with a 2D heatmap (Page 16, Figure 3)
2. Added color-coded performance tiers (high/medium/low)
3. Included a detailed caption explaining each element (Page 16, Lines 325-335)
4. Added a companion schematic diagram (Page 17, Figure 4) to illustrate the underlying evaluation framework

We believe these changes make our results much more accessible to readers.

---

**Summary of Major Changes:**

1. **Expanded Introduction**: Added explicit research gap statement and comparison with previous studies (Page 3-4)
2. **Increased Sample Size**: From 50 to 75 articles with balanced domain representation (Page 8)
3. **Enhanced Methodology**: Detailed evaluation criteria and inter-rater reliability analysis (Page 10-11)
4. **Improved Visualizations**: Redesigned Figure 3 and added Figure 4 for clarity (Page 16-17)
5. **Strengthened Discussion**: Added limitations section and future research directions (Page 21-22)

We believe these revisions have significantly strengthened the manuscript and addressed all reviewer concerns. We hope the revised manuscript is now suitable for publication in the Journal of Artificial Intelligence Research.

Thank you for your consideration.

Sincerely,

Dr. Jane Doe
Corresponding Author
Department of Computer Science
University of Example
Email: [email protected]