Fundamentals 6 min read

How to Effectively Respond to Vague, Low‑Quality Negative Reviews

The article outlines a step‑by‑step approach for handling vague, low‑quality negative peer‑review comments by breaking down identifiable issues, politely requesting clarification, and presenting factual evidence to the area chair without attacking the reviewer.

Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
How to Effectively Respond to Vague, Low‑Quality Negative Reviews

The MLNLP community released the "Paper‑Rebuttal‑Tips" project to help researchers address common vague negative reviews by categorising frequent reviewer concerns and offering reusable response strategies.

Vague negative comments such as “limited contribution, insufficient experiments, unclear method” often lack specific guidance, making them the hardest to answer; the article warns against directly denying the reviewer or questioning their expertise.

Examples of discouraged replies include statements like “the review is too vague for us to respond,” “the reviewer did not understand the work,” or “the comment lacks basis and cannot be a rejection reason.” These approaches turn the rebuttal into a confrontation.

Recommended reply structure: Thank the reviewer, acknowledge the feedback, and then address each identifiable issue: Contribution: explain the core contribution and how it differs from prior work. Experiments: describe added or revised experiments, ablation studies, or comparisons and their results. Method clarity: clarify definitions, workflow, and implementation details. For generic comments, politely request more specific concerns.

The core guidance is summarised in three points:

1. Identify and answer each recognizable problem

Even when the review is vague, break it down into possible issues such as contribution, experiments, method clarity, distinction from existing work, and limitations.

2. Politely ask for clarification on generic remarks

“Because the comment lacks specific details, we may be unable to fully assess your concern.”

This phrasing is more acceptable than accusing the reviewer of poor quality.

3. When informing the Area Chair, present only facts and evidence

“The review contains several generic statements without concrete details. We have addressed all identifiable points and added supporting evidence, and we kindly ask the AC to consider these responses.”

The article concludes with a one‑sentence summary: to handle vague negative reviews, address identifiable issues individually, request clarification for generic comments, and present factual evidence to the AC without evaluating the reviewer’s competence.

Project repository link
Project repository link
Original Source

Signed-in readers can open the original source through BestHub's protected redirect.

Sign in to view source
Republication Notice

This article has been distilled and summarized from source material, then republished for learning and reference. If you believe it infringes your rights, please contactadmin@besthub.devand we will review it promptly.

academic writingpeer reviewresearch communicationpaper rebuttalreview response
Machine Learning Algorithms & Natural Language Processing
Written by

Machine Learning Algorithms & Natural Language Processing

Focused on frontier AI technologies, empowering AI researchers' progress.

0 followers
Reader feedback

How this landed with the community

Sign in to like

Rate this article

Was this worth your time?

Sign in to rate
Discussion

0 Comments

Thoughtful readers leave field notes, pushback, and hard-won operational detail here.