R&D Management 5 min read

How to Address Reviewer Comments About Insufficient Theoretical Analysis

The article explains why simply citing strong experimental results does not satisfy reviewers who question a method's theoretical justification, and provides a structured rebuttal strategy that clarifies core mechanisms, underlying assumptions, and the method's applicability limits.

Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
How to Address Reviewer Comments About Insufficient Theoretical Analysis

Problem

Reviewers may comment: “The method lacks theoretical explanation; it is unclear why it works.” The comment does not demand a formal theorem but expects an intuition, the conditions under which the method works, and possible failure cases.

Undesired reply

Thank you for the suggestion. Our method performs well in experiments, which shows it is effective.

This reply is insufficient because it equates good experimental results with a clear mechanism.

Reviewer concerns

What is the core intuition behind the method?

Why does component M1 lead to improvement M2 ?

What assumptions does the method rely on?

How do key steps relate to the objective function or representation space?

In which scenarios might the method fail?

Recommended reply

Thank you for the suggestion. We agree that a theoretical explanation helps understand the method’s effectiveness. The core intuition of our approach is XXX . Under the assumption YYY , M1 causes ZZZ , which enables M2 to achieve … We will add an analytical discussion in the revision, covering the core assumptions, the relationship between key steps and the objective/representation space, and the boundary conditions where the method may break down.

Core rebuttal structure

Address “insufficient theoretical analysis” by elaborating on three aspects.

1. Explain the core mechanism

Module A enhances the XXX representation, thereby alleviating the XXX problem.

2. State the basic assumptions

When the XXX assumption holds, the mechanism can more stably optimize the XXX objective.

3. Clarify the applicability boundary

If the XXX condition is violated, the method’s gains may diminish.

Providing these explanations satisfies the reviewer and makes the paper appear more rigorous.

Project address

GitHub repository: https://github.com/MLNLP-World/Paper-Rebuttal-Tips

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Machine Learning Algorithms & Natural Language Processing
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