How to Write Winning Rebuttals for NeurIPS: 28 Open‑Source Case Studies

Purpose

The open‑source repository https://github.com/MLNLP-World/Paper-Rebuttal-Tips collects the most frequent reviewer comments from top AI conferences and provides a structured analysis for each.

Organization of the resource

28 reviewer‑comment categories (e.g., innovation, methodological complexity, insufficient experiments, misunderstanding of contributions).

For every category the repository answers three questions:

What does the reviewer truly care about?

Why is a particular type of reply persuasive?

How should the reply be logically structured?

Each entry contains:

A detailed analysis of the comment.

A concrete reply strategy.

An example rebuttal.

Practical notes and caveats.

Concrete example

Reviewer comment: “Each module has an ablation study, so the design is reasonable.” The deeper concerns identified are:

Why are all modules necessary?

Do the modules truly cooperate rather than being a simple concatenation?

Does the added complexity bring sufficient performance gain?

The recommended three‑step reply process is:

Identify the reviewer’s core concern (e.g., necessity of modules).

Explain the design motivation or provide additional evidence (e.g., extra ablation results, synergy analysis).

Articulate the benefit to the overall contribution (e.g., performance improvement justified by the added complexity).

Content of the repository

Each case is organized around the following aspects:

Problem‑driven analysis : clarify the reviewer’s underlying question.

Design motivation : describe why the proposed components were chosen.

Module synergy : show how components interact and why the combination is more than a sum of parts.

Experimental validation : present additional ablation or quantitative results that directly address the concern.

The repository also groups the cases into higher‑level themes such as:

Innovation, motivation, theory, and scope.

Complexity versus performance trade‑offs.

Insufficient experimental evidence.

Misinterpretation of the paper’s contributions.

Transferability

Although the examples are drawn from NLP and machine‑learning venues (ACL, EMNLP, NeurIPS, ICML, ICLR, CVPR, etc.), the logical framework—problem identification, evidence provision, and benefit articulation—is applicable to other domains such as computer vision, multimodal learning, and data mining.

Key takeaways

Move beyond template sentences; construct replies that directly address the reviewer’s true concern.

Explain the rationale behind design choices and back them with concrete evidence.

Keep the rebuttal concise yet logically complete within the limited word budget.