How to Effectively Answer Reviewer Concerns About Method Complexity and Computational Overhead

Undesirable response

Many authors reply with a generic statement such as “although the cost is high, the performance gain makes it worthwhile.” This answer is weak because it does not present any verifiable cost analysis.

Recommended response

Thank you for the comment. We agree that practical deployment requires balancing performance and cost. The additional overhead of our method mainly stems from XXX , with a complexity of XXX . Compared with the baseline, training/inference time increases by approximately XXX% , while achieving a performance improvement of XXX . Furthermore, the overhead is controllable: It only appears during training/pre‑processing/offline construction and does not affect online inference. The relevant steps can be parallelized or cached. In large‑scale settings, the overhead represents a small fraction of the overall training cost. For multi‑stage pipelines, our method follows a “one‑time investment, long‑term reuse” design, allowing the upfront cost to be amortized over subsequent inference or deployment. We will add detailed complexity analysis, runtime, memory usage, and amortization discussion in the revised manuscript.

Core questions to address

1. Where does the overhead come from?

Examples include additional retrieval steps, multi‑turn reasoning, extra model calls, complex matching or ranking modules, offline index construction, and auxiliary training objectives.

2. How large is the overhead?

Provide concrete metrics such as time complexity, inference time, training time, memory consumption, parameter count, FLOPs, extra API calls, and percentage increase relative to the baseline.

3. Is the overhead controllable?

If the extra cost occurs mainly offline, clarify that it does not impact real‑time inference. Mention possibilities for parallelization, caching, or amortization across multiple inferences.

One‑sentence summary: Avoid vague claims that “the effect justifies the cost”; instead, explicitly analyze the source, magnitude, and controllability of complexity, time, memory, and amortization to convincingly address reviewer concerns.

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