How to Write a Top-Conference Rebuttal Under Tight Time and Word Limits
Acadwrite Team · Last updated July 9, 2026
A machine learning or AI conference rebuttal demands a different strategy from a point-by-point journal response: words are limited, the window lasts only days, and the goal is to raise the score rather than close every comment. This guide explains what to prioritize and how to write under those constraints, focusing on concerns such as missing recent comparisons, incremental contribution, and unclear novelty that require real literature reading within a very short window.
A response letter for a major or minor journal revision uses a different structure; see how to respond to journal reviewers.
1. Three constraints on a rebuttal
Three constraints determine the conference rebuttal strategy and make it fundamentally different from a journal response:
Most top conferences impose a hard word or character limit. You cannot respond at length to every point and must focus on what matters.
The rebuttal window often lasts only a few days. You can add only experiments that finish in that period, and there is no time for a slow literature search.
Respond to the main issues affecting the score and support each response with a revision location or additional evidence.
2. Rebuttal structure and timeline
Group by reviewer: use sections such as “To R1 / To R2 / To R3.” Answer the one or two score-critical points first in each group and combine minor points into short responses.
Promise only changes you can deliver: put small experiments or comparison tables that can finish within the window first. For anything infeasible, define the limitation honestly and give a plan and partial results.
Prioritize when words run out: reserve words for the causes of score reductions, such as novelty and methodology. One sentence promising a camera-ready revision is enough for writing or format issues.
Receive reviews → rebuttal window of a few days for writing and submission → reviewer-author discussion to answer follow-ups and seek score changes → reviewers update scores → final decision. Every stage is short, so do the work most likely to affect scores first.
3. Five comment types and how to respond
Classifying comments makes priorities clear. Literature and evidentiary concerns both weigh heavily on novelty judgments and consume the most time, while the rebuttal window is extremely short—exactly where fast retrieval helps.
| Comment type | Typical wording | Can literature retrieval address it? |
|---|---|---|
| Literature | incremental over X, missing comparisons with recent baselines, novelty unclear | Directly (and retrieval matters most in a short window) |
| Evidence for claims | A claim or conclusion lacks support and needs citations | Yes (find supporting literature quickly) |
| Method or experiment | Missing baseline, missing ablation, requested experiment | Only as support; add only experiments that finish in days |
| Writing and presentation | Unclear prose, figures, or structure | No; promise a camera-ready revision |
| Formatting | Length, citations, or template | No |
4. Literature concerns: retrieve evidence quickly
“Incremental over X,” “missing comparisons with recent methods,” and “novelty unclear” directly lower scores but can only be rebutted by reading recent work. With a rebuttal window of just a few days, a slow search is impossible; this is the category that most needs acceleration.
Only a few days? Put the reviewer's exact words into a workspace
Within the short window, an Acadwrite workspace can help you find the recent work that needs comparison, extract its methods and differences, and build the comparison, then turn it into a concise response under the word limit. Compressing the find-read-distill step leaves more time for language that can actually change the score.
Example: a borderline review claiming an incremental contribution and missing comparisons
Suppose a paper proposes a new LoRA variant for parameter-efficient fine-tuning and is submitted to a machine learning conference. During rebuttal, one reviewer gives it a borderline score and writes:
Reviewer 3 (Rating: 4, borderline). The proposed method is incremental over LoRA. Recent work such as DoRA and AdaLoRA already improves upon LoRA via weight decomposition and adaptive budget allocation. Without comparison to these, the contribution is unclear.
- DoRA (Liu et al., ICML 2024 Oral) decomposes pretrained weights into magnitude and direction, updates direction with LoRA, consistently outperforms LoRA on LLaMA, LLaVA, and VL-BART, and adds no inference overhead.
- AdaLoRA (Zhang et al., ICLR 2023) uses importance-scored SVD parameterization to allocate the rank budget adaptively across layers and prune unimportant singular values. It performs particularly well under small budgets and is integrated into Hugging Face PEFT.
DoRA changes magnitude-direction weight decomposition; AdaLoRA changes rank-budget allocation across layers. The paper changes a different mechanism and is orthogonal and composable with them rather than a replacement—the central sentence rebutting the incremental-contribution claim.
To R3 (novelty vs. DoRA / AdaLoRA). Thanks. DoRA (Liu et al., ICML'24) decomposes weights into magnitude/direction; AdaLoRA (Zhang et al., ICLR'23) reallocates rank budget across layers by importance. Our method is orthogonal to both and can be combined with them. We added Table R1 (rebuttal PDF) comparing all three under matched parameter budgets, and show it is complementary when stacked on DoRA. We will integrate this discussion into Related Work.
This is an example framework; use your own experimental values for Table R1 and similar details. The key is that a window of only days and a strict word limit make “find the right recent work quickly + distill the difference into one sentence” even more urgent than in a journal response—the situation where literature retrieval has its highest value.
Frequently asked questions
What should I cut when the rebuttal word limit is too short?
Prioritize concerns most likely to affect the score—usually comments from low-scoring reviewers that directly question novelty or the core method. Use one sentence to promise camera-ready fixes for writing or formatting and reserve the limited words for substantive issues.
What if a reviewer clearly misread the paper?
Clarify politely, attribute the problem to your own lack of clarity, and state where you will add an explanation. Do not accuse the reviewer of misunderstanding. Other reviewers and the area chair also read the rebuttal, so tone affects the overall impression.
What if the requested experiment cannot finish in a few days?
Say so honestly, provide whatever partial result can be produced within the window—even a small-scale or preliminary one—and explain the plan and expected full experiment. Specific, credible partial evidence is better than an empty promise or evasion.
Must I answer every point from every reviewer?
Cover each reviewer's main concerns when the length limit allows, but do not write at length about every small point. Group by reviewer (To R1 / R2 / R3), answer the one or two score-critical issues first, and combine minor points into brief replies.
Can I add information after the rebuttal?
Many conferences have a reviewer-author discussion period after rebuttal. You can answer follow-up questions and add clarification. Proactively and concisely addressing unresolved concerns creates another opportunity to change a score.
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Accelerate the few-day window with a workspace
Retrieve recent work quickly, distill the differences, and draft a rebuttal under the word limit.