Mastering Reviewer Comments in Deep Learning and LLM Research
Publishing cutting-edge research in Deep Learning and Large Language Models (LLMs) is a rigorous process. A critical stage is responding to reviewer comments. Effectively addressing feedback not only improves your paper but also demonstrates your commitment to scientific discourse and strengthens your chances of acceptance.
Understanding the Reviewer's Role
Reviewers are experts in the field tasked with evaluating the novelty, significance, technical soundness, and clarity of your work. Their comments are intended to help you refine your research and presentation. Approaching their feedback with a collaborative mindset is key.
To evaluate the novelty, significance, technical soundness, and clarity of the research and help refine the paper.
Strategies for Responding to Reviewer Comments
A structured approach to responding ensures all points are addressed thoroughly and professionally. This typically involves creating a 'response letter' or 'rebuttal' document.
Deconstruct and Categorize Feedback
Read all comments carefully. Categorize them into major issues (e.g., fundamental flaws, missing experiments) and minor issues (e.g., typos, clarity suggestions). This helps prioritize your efforts.
Major issues and minor issues.
Drafting Your Response Letter
Your response letter should be polite, respectful, and detailed. For each comment, clearly state how you have addressed it. If you disagree with a comment, provide a well-reasoned explanation.
Address each reviewer comment systematically.
For every comment, create a clear response. If you made changes, specify them. If you disagree, explain why respectfully.
Structure your response letter by addressing each reviewer's comments individually. Use a clear format, perhaps numbering each comment and then providing your response. For comments that require changes, explicitly state what you have done (e.g., 'We have added a new experiment in Section 4.2 to address this point,' or 'We have clarified the explanation of the loss function on page 5, line 120.'). If you believe a reviewer has misunderstood a point or if you disagree with their assessment, provide a polite and evidence-based rebuttal. Avoid being defensive; focus on constructive dialogue.
Implementing Changes in the Manuscript
When you make changes to the manuscript, highlight them (e.g., using track changes or colored text) to make it easy for the reviewers and editor to see your revisions. Ensure that your revised manuscript is consistent and polished.
Treat the response letter as another piece of your research output – clarity, accuracy, and professionalism are paramount.
Common Pitfalls to Avoid
Common mistakes include not addressing all comments, being dismissive, making superficial changes, or introducing new errors. Thoroughness and respect are your best tools.
| Action | Effective Approach | Ineffective Approach |
|---|---|---|
| Addressing Comments | Address every comment systematically and respectfully. | Ignoring or superficially addressing comments. |
| Disagreement | Provide polite, evidence-based rebuttals. | Being defensive or dismissive. |
| Manuscript Changes | Clearly highlight all revisions. | Making changes without clear indication or introducing inconsistencies. |
Specific Considerations for Deep Learning and LLMs
In fields like Deep Learning and LLMs, reviewers often scrutinize experimental setup, hyperparameter choices, ablation studies, and the interpretation of results. Be prepared to justify your methodological decisions and provide clear explanations for your model's behavior.
A typical reviewer comment might ask for clarification on the training data distribution or the impact of a specific hyperparameter. Your response should detail the data preprocessing steps, the rationale behind hyperparameter selection, and potentially present additional experiments or visualizations to support your claims. For LLMs, this could involve discussing prompt engineering strategies, evaluation metrics specific to generative tasks, or ethical considerations.
Text-based content
Library pages focus on text content
Consider adding more ablation studies to demonstrate the contribution of each component of your model. If your LLM has safety or bias concerns, reviewers will likely ask for detailed mitigation strategies and evaluations.
Final Polish and Submission
Before submitting your revised manuscript and response letter, proofread everything meticulously. Ensure that your response letter is well-organized, easy to read, and directly answers all points raised by the reviewers. A well-crafted response can significantly increase your chances of acceptance.
Learning Resources
Provides practical advice from Elsevier on structuring and writing effective responses to reviewer feedback.
A blog post offering actionable tips and strategies for authors navigating the peer review process and responding to comments.
An article from Nature discussing the importance and techniques for crafting a strong rebuttal to reviewer comments.
A humorous yet insightful video offering advice on how to handle reviewer feedback, particularly from the often-challenging 'Reviewer 2'.
Editage provides a comprehensive guide on the essential components and best practices for writing a rebuttal letter.
Nature's overview of the peer review process, including the role of reviewers and authors in refining research.
American Journal Experts offers a detailed guide on how to effectively respond to reviewer comments and revise manuscripts.
While focused on rejection, this article offers valuable insights into how to interpret and learn from reviewer feedback, applicable to revisions as well.
An overview of the peer review process from the publisher's perspective, highlighting the author's role in revisions.
Enago Academy offers practical advice on structuring and writing a compelling rebuttal letter to address reviewer concerns.