What Is the Best Way to Respond to a Reviewer Who Asked for Some More Comparisons When There Are Already Enough Rival Algorithms in the Paper? A Practical Publication Guide for PhD Scholars

For many scholars, one reviewer comment can feel harder than writing the paper itself. A common example is this: what is the best way to respond to a reviewer who asked for some more comparisons when there are already enough rival algorithms in the paper? This question matters because it sits at the intersection of scientific judgment, publication strategy, and academic diplomacy. Many PhD scholars and early-career researchers worry that refusing a reviewer may look defensive. At the same time, adding endless experiments can waste time, dilute the contribution, and even create new weaknesses in the manuscript. That tension is real. It affects researchers across disciplines, especially in data science, computer science, engineering, management, and interdisciplinary quantitative work.

The modern publication environment adds even more pressure. Peer review remains central to scholarly communication, and journals expect careful, point-by-point responses during revision. Springer Nature states that when authors revise, they should address all reviewer points, describe major revisions clearly, and explain why they disagree when they believe a suggested change would not improve the manuscript. APA also advises authors to respond to every comment and distinguish reviewer remarks from author responses. In practice, that means authors should not ignore a request for more comparisons, even when the request seems excessive. They should answer it thoughtfully, respectfully, and with evidence. (Springer Nature)

This issue is especially important in algorithmic papers. Reviewers often ask for more baselines, more datasets, more ablation studies, or more rival methods. Some of those requests are valid. Others are open-ended. Elsevier’s publication guidance emphasizes that reviewers and editors look for whether the most relevant prior work has been cited and compared. That phrasing matters. The standard is not “compare with everything ever published.” The standard is to compare with the most relevant work. Likewise, Springer Nature’s author guidance notes that additional analyses should be performed when they improve the paper, but authors may explain clearly when a requested addition would not materially improve the manuscript. (Elsevier Researcher Academy)

For PhD scholars, this distinction can save weeks or months of unnecessary work. It also protects the integrity of the paper’s scope. A well-framed response shows maturity. It signals that the authors understand the literature, the paper’s contribution, and the journal’s standards. It also shows that the authors can separate a meaningful scientific gap from a request for unlimited benchmarking. That is a crucial academic skill.

There is also a practical reason to learn this well. Springer Nature notes that around 97% of accepted submissions require at least one revision. In other words, revision is normal, not exceptional. If you plan to publish regularly, you must become skilled at writing reviewer responses that are calm, precise, evidence-based, and strategically persuasive. In addition, Nature highlights that peer review operates on mutual trust, while COPE emphasizes objective and constructive evaluation. A good author response should reflect the same spirit. (Springer Nature)

At ContentXprtz, we often see a pattern. Researchers either overcomply and add too many weak comparisons, or they under-explain and reject the comment too bluntly. Neither approach works well. The better path is measured. You acknowledge the reviewer’s concern. You show that you understand the scientific intent behind the request. You either add a focused improvement or justify, with logic and citations, why the current comparison set is already sufficient. That is the heart of effective academic editing, PhD support, and research paper assistance.

Why Reviewers Ask for More Comparisons in Algorithm Papers

A request for more comparisons usually comes from one of five concerns. First, the reviewer may worry that the novelty claim is overstated. Second, the reviewer may feel that the baseline selection is incomplete. Third, the reviewer may suspect that the chosen rivals are weak or outdated. Fourth, the reviewer may want evidence that the proposed method performs well under broader conditions. Fifth, the reviewer may simply be signaling uncertainty and using “more comparisons” as a generic way to ask for stronger validation.

Therefore, before drafting your response, you should diagnose the real concern behind the comment. That diagnosis shapes the quality of your rebuttal. If the concern is about fairness, you may need to clarify why the chosen methods are the accepted benchmarks. If the concern is about recency, you may need to cite newer papers and explain why they are not directly comparable. If the concern is about scope, you may need to explain that the study addresses a specific problem setting rather than all variants of the broader problem.

This is where strong academic editing services can help. A reviewer comment often looks simple on the surface, but it usually reflects a deeper issue in framing. The goal is not just to answer the sentence. The goal is to answer the concern.

The Best Principle: Respond to the Scientific Concern, Not Just the Literal Request

When scholars ask, what is the best way to respond to a reviewer who asked for some more comparisons when there are already enough rival algorithms in the paper?, the best answer is this: respond to the scientific rationale behind the request, not merely the words.

That means you should do three things in sequence.

First, acknowledge the value of the comment.

Second, show that your current comparison set is relevant, representative, and sufficient for the paper’s objective.

Third, either make a targeted improvement or provide a justified limitation statement.

This approach works because editors usually want to see reasoned engagement, not blind compliance. APA’s guidance on response letters supports a comment-by-comment format. Springer Nature also recommends clear explanation when authors disagree with a requested addition. (APA Style)

When You Should Add More Comparisons

You should add more comparisons if one or more of the following are true:

• A key state-of-the-art baseline is missing.
• The omitted method is widely recognized in your exact subproblem.
• The comparison can be added fairly with available code, data, and settings.
• The new result would likely strengthen the paper’s credibility.
• The reviewer’s request reveals a genuine gap in validation.

For example, imagine your paper proposes a graph neural network for node classification, but you compare it only with older machine learning methods and ignore the dominant recent GNN baselines. In that case, the reviewer is right. Adding more comparisons is not a concession. It is a necessary correction.

Similarly, if your paper claims state-of-the-art performance, then the burden of comparison becomes higher. Strong claims require strong benchmarking. In such cases, expanding the comparison table may be the fastest path to acceptance.

When You Should Not Add Endless Comparisons

However, you do not need to add endless comparisons simply because a reviewer asks for “some more.” That phrase is vague. A paper is not stronger just because the table is longer. In fact, too many irrelevant or weakly aligned baselines can confuse readers and distract from the central contribution.

You may reasonably decline to add more comparisons when:

• The requested methods solve a different task.
• They rely on different data assumptions.
• They require unavailable proprietary code or inaccessible datasets.
• They are not established baselines in your problem setting.
• They would expand the paper beyond its stated scope.
• The manuscript already compares against representative classical and recent methods.

Elsevier’s guidance focuses on comparison with the most relevant prior work, not an unlimited list of rivals. That distinction gives authors a principled basis for a respectful rebuttal. (Elsevier Researcher Academy)

A Strong Response Framework You Can Use

A high-quality response usually follows this structure:

1. Thank the Reviewer

Start with appreciation. This lowers friction and signals professionalism.

Example:

We thank the reviewer for this helpful suggestion regarding additional algorithmic comparisons.

2. Clarify the Selection Logic

Explain how you chose the comparison methods.

Example:

Our baseline selection was designed to include representative traditional methods, strong recent methods, and the most commonly cited competitors for the same task setting.

3. Show Why the Current Set Is Already Strong

State the scope and relevance of the existing baselines.

Example:

The current manuscript already compares the proposed method against eight rival algorithms, including three recent state-of-the-art approaches and two domain-standard baselines widely used for this dataset family.

4. Address the Request Constructively

Choose one of two routes.

Route A: Add one or two focused comparisons.

Route B: Explain why further comparisons would not be scientifically appropriate.

5. Strengthen the Manuscript Anyway

Even when you decline the full request, improve the paper slightly. For example, add a sentence in the methods section explaining baseline selection criteria, or add a limitation note in the discussion. That shows responsiveness.

The Most Effective Wording for a Polite Rebuttal

Here is a model response that often works well:

Reviewer Comment: The paper should compare with more rival algorithms.
Response: We thank the reviewer for this valuable suggestion. We agree that comparative evaluation is important for establishing the contribution of the proposed method. In the revised manuscript, we have clarified the rationale for baseline selection in Section 4.2. Specifically, we selected methods that are most relevant to the same task setting, data assumptions, and evaluation protocol. The current comparison already includes representative conventional baselines, strong recent methods, and widely cited task-specific competitors. We respectfully note that adding further algorithms outside this problem setting may not yield a fair or meaningful comparison, because several of those methods rely on different input assumptions and training conditions. To strengthen the manuscript, we have added a clearer explanation of the chosen baselines and expanded the discussion of comparative scope and limitations on Page X, Lines Y-Z.*

This wording does five things well. It thanks the reviewer. It validates the principle. It clarifies the method. It explains the limit. It still improves the paper.

What Editors Usually Appreciate

Editors tend to appreciate responses that are specific, restrained, and easy to verify. They do not want emotional language. They do not want vague claims such as “we already compared enough.” They want a reasoned explanation supported by the manuscript itself.

Therefore, your response should refer to:

• The task setting
• The baseline selection criteria
• The fairness of the comparison
• Practical constraints, if relevant
• The exact manuscript changes made

This is why point-by-point revision letters matter so much. APA and Springer Nature both emphasize organized, comment-by-comment responses. (APA Style)

A Better Strategy Than Saying “We Already Have Enough”

Do not write:

We already compared enough algorithms.

That sounds dismissive.

Write this instead:

The manuscript already includes a representative set of baselines chosen to reflect classical, recent, and task-specific competitors under the same evaluation setting. We have now clarified this selection rationale in the revised text.

That sounds thoughtful and defensible.

How to Decide Whether the Reviewer Is Right

Use this quick academic checklist:

Ask Yourself These Five Questions

1. Is there a missing baseline that readers would genuinely expect?
2. Are my current rivals truly the strongest relevant comparators?
3. Are the requested methods solving the same problem under the same assumptions?
4. Can I run the comparison fairly and reproducibly?
5. Would an editor see my current table as representative?

If your answer to Questions 1, 2, or 5 is “no,” add comparisons.

If your answer to Questions 3 or 4 is “no,” explain why a broader comparison would be unfair or infeasible.

Real Example: Weak Response vs Strong Response

Weak Response

We disagree. The paper already has many baselines.

Why it fails: it offers no reasoning, no scope clarification, and no manuscript change.

Strong Response

We thank the reviewer for highlighting the importance of comparative validation. Our current experiments include representative classical, recent, and task-specific baselines that address the same prediction setting and use the same input assumptions. To make this clearer, we added a paragraph in the experimental setup explaining our baseline selection criteria. We respectfully note that several additional methods suggested in the broader literature are designed for different data availability settings and would therefore not provide a fair head-to-head comparison under the present protocol.

Why it works: it is respectful, scientific, and verifiable.

How to Improve the Paper Even If You Decline the Request

This is one of the smartest revision strategies. Even if you do not add more experiments, you can still strengthen the manuscript by adding:

• A short paragraph on why these baselines were selected
• A sentence stating the scope of comparison
• A limitation note about methods not included
• A citation to recent related studies
• A supplementary note, if helpful

That small revision often satisfies both reviewers and editors because it removes ambiguity.

Recommended Outbound Academic Resources

For scholars who want to refine their revision practice, these resources are especially useful:

• Springer Nature guidance on revising and responding to reviewers (Springer Nature)
• APA Style guidance on response to reviewers (APA Style)
• APA publishing tips on peer review and detailed response letters (American Psychological Association)
• COPE guidance on objective and constructive peer review (Publication Ethics)
• Nature Portfolio peer review policy (Nature)

Where ContentXprtz Can Support You

If you are struggling to draft a convincing rebuttal, structured academic support can save time and reduce risk. Many authors know the science but find it difficult to phrase disagreement diplomatically. That is where expert revision support becomes valuable.

ContentXprtz offers tailored help for scholars who need research paper writing support, PhD thesis help, student academic writing services, and specialist assistance for book authors or corporate research communication. The goal is not to overwrite your voice. The goal is to strengthen your academic argument, improve your reviewer response, and protect publication quality.

Frequently Asked Questions

FAQ 1: Can I politely disagree with a reviewer who asks for more algorithm comparisons?

Yes, you can. In fact, sometimes you should. Reviewers are important, but they are not automatically correct on every request. Journals generally expect authors to engage seriously with reviewer comments, not to obey them mechanically. A polite disagreement becomes appropriate when the requested comparisons are outside the paper’s scope, involve incompatible assumptions, or would not produce a fair scientific test. The key is tone and evidence. You should never sound irritated or dismissive. Instead, acknowledge the value of the concern, explain your baseline selection logic, and show why the current comparison set is already representative. If possible, improve the manuscript by clarifying that logic in the experimental design section. That way, you show responsiveness even while declining the full request. This is often the most effective middle path. For PhD scholars, this is a critical lesson. Good publication practice is not blind compliance. It is reasoned academic judgment expressed respectfully. Strong academic editing can also help you phrase disagreement in a way that protects both scientific rigor and editor confidence.

FAQ 2: How many rival algorithms are enough in a research paper?

There is no universal number. The right number depends on the paper’s claim, field norms, journal expectations, and the exact problem being studied. A highly novel method with a strong performance claim usually needs more extensive comparisons. A narrower methodological paper may need fewer, provided the chosen baselines are clearly justified. What matters most is representativeness, not volume. Your comparison set should include standard baselines, strong recent methods, and the most relevant task-specific competitors. If those are present, then adding more algorithms may not improve the paper. In some cases, a table with six well-chosen baselines is much stronger than a table with fifteen loosely related methods. Reviewers often respond well when authors explain why each class of baseline was included. That explanation reduces ambiguity and increases trust. Therefore, instead of asking, “How many is enough?” ask, “Does this set allow a reader to judge novelty, fairness, and practical advantage?” If the answer is yes, your comparison section is probably in good shape.

FAQ 3: What if the reviewer does not name the extra algorithms they want?

This is very common. A vague comment such as “compare with more algorithms” can feel frustrating because it gives no clear action item. However, you can still answer it effectively. Start by identifying whether your current baselines already include the major categories of relevant competitors. Then revise the paper to explain your selection logic more clearly. In your response letter, mention that the manuscript already compares against representative classical, recent, and task-specific methods under the same evaluation setting. You can also add that methods outside that setting may not support fair comparison. If you think one important recent paper may have been overlooked, add it. If not, strengthen the explanation rather than expanding the experiment list without direction. This approach is often enough because the reviewer’s real complaint may be about clarity rather than quantity. Many reviewers use vague language when they feel uncertain. Your job is to remove that uncertainty. A polished response letter, supported by strong academic editing, can often turn a vague objection into a resolved issue.

FAQ 4: Should I add comparisons in the main paper or the supplement?

That depends on the journal format, the importance of the additional experiment, and the paper’s page constraints. If the new comparison is central to the paper’s validity, it should go in the main manuscript. Editors and reviewers should not need to search a supplement for a result that affects the core claim. However, if the additional comparison is helpful but secondary, then placing a fuller version in the supplement can work well. In that case, summarize the result in the main paper and direct readers to the supplementary file for details. This is often a smart compromise when reviewers ask for extra breadth but the manuscript already has heavy space pressure. Some journals also enforce strict word or page limits, and Elsevier notes that revisions should not substantially exceed those limits even when reviewers ask for more information. That makes strategic placement even more important. A good rule is simple: primary validity stays in the main text, supportive depth can go in the supplement. When in doubt, ask what a skeptical reader would need to see first to trust the paper.

FAQ 5: Is it risky to say that a requested comparison would be unfair?

It is not risky if you explain it clearly and professionally. In fact, fairness is one of the strongest reasons to decline a comparison. Not all algorithms operate under the same assumptions. Some require extra data. Others use different supervision levels, different preprocessing pipelines, different computational budgets, or different access to labels. A head-to-head comparison across mismatched conditions can mislead readers. Therefore, if a reviewer asks you to compare against a method that belongs to a different task formulation, you can respectfully explain that the comparison would not be methodologically fair. However, your explanation must be concrete. Do not merely state “unfair.” State why. Mention the specific mismatch, such as different input assumptions, unavailable features, non-comparable training conditions, or inconsistent evaluation protocols. Then strengthen the paper by stating these scope boundaries in the methods or limitations section. This turns a potential disagreement into a methodological clarification. Editors usually appreciate that level of precision because it protects the integrity of the published record.

FAQ 6: How should I respond if I cannot run the requested algorithm because the code is unavailable?

You can say so, but you should do it carefully. Unavailable code is a practical limitation, not always a sufficient scientific justification on its own. Therefore, frame the issue in a broader way. First, acknowledge the reviewer’s suggestion. Second, state that you assessed the possibility of adding the comparison. Third, explain that the method lacks accessible implementation, reproducible settings, or enough procedural detail for a fair reimplementation within the current revision cycle. Fourth, clarify that you strengthened the manuscript in other ways, such as by citing the method in related work, discussing its conceptual relevance, or clarifying the scope of included baselines. This approach shows effort rather than avoidance. It also protects you from appearing selective. If possible, note that the current comparison set already includes representative methods from the same family. That makes your response stronger. Many PhD scholars feel guilty when they cannot satisfy every experimental request. They should not. A journal revision is about scientific judgment, feasibility, and fairness, not endless expansion under unrealistic conditions.

FAQ 7: Can more comparisons actually weaken my paper?

Yes, they can. More experiments do not automatically mean a stronger manuscript. Sometimes extra comparisons introduce inconsistent protocols, distract from the main contribution, create noisy tables, or reveal marginal issues that do not matter to the core research question. In other cases, authors rush to add weakly aligned baselines simply to satisfy a reviewer, and the paper becomes harder to read. This is especially risky in PhD-level work, where a dissertation chapter or journal article may already contain dense methodological material. A bloated experimental section can reduce clarity. It can also shift the paper from focused to unfocused. The real goal is persuasive validation, not maximal accumulation. Therefore, each comparison should answer a clear scientific question. Does it establish superiority, robustness, efficiency, generalizability, or fairness? If it does none of these, it may not belong in the paper. This is why strong editorial judgment matters. Good academic support does not simply add content. It helps authors decide what strengthens the argument and what dilutes it.

FAQ 8: What is the best tone for a rebuttal letter when I disagree?

The best tone is calm, respectful, and evidence-led. Think of the rebuttal letter as a professional academic dialogue, not a defense brief. You want the reviewer and editor to feel that you took the comment seriously, even if you ultimately disagree. That means you should avoid phrases that sound absolute, irritated, or dismissive. Do not write, “The reviewer is wrong,” or, “This request is unnecessary.” Instead, write, “We respectfully note,” “To ensure a fair comparison,” or, “Given the scope of the present study.” Those small shifts matter. They reduce friction and preserve credibility. Tone is especially important when the issue involves methodological boundaries rather than factual error. A good rebuttal letter also balances firmness and flexibility. Where you disagree, explain why. Where you can improve the paper, do so. This combination often persuades editors because it signals maturity. Many manuscripts are judged not only by their data but also by the professionalism of their revision materials. A strong tone can meaningfully affect that impression.

FAQ 9: How can I make my baseline selection look more convincing in the paper itself?

The easiest way is to stop treating baseline choice as obvious. Many manuscripts list comparison methods without ever explaining why they were chosen. That creates room for reviewer doubt. Instead, add a short baseline-selection paragraph in your methods or experimental setup section. Explain that the chosen methods represent standard classical baselines, widely used recent approaches, and task-specific state-of-the-art comparators under the same evaluation conditions. If relevant, also explain why certain popular methods were not included, such as mismatch in task setting, unavailable implementation, or incompatible assumptions. This one paragraph can prevent many reviewer comments before they arise. You can also improve table design. Group baselines by category. Mark recent methods clearly. State whether results come from original papers, reproduced code, or your own reruns. That transparency improves trust. In publication support work, this is one of the most common fixes we recommend because it addresses reviewer concern at the source rather than only in the rebuttal phase.

FAQ 10: When should I seek professional editing or reviewer-response help?

You should consider expert help when the science is sound but the framing is not landing well. That includes situations where reviewer comments feel repetitive, where your response letter sounds defensive, where you struggle to justify scope, or where English clarity may affect how your argument is received. Professional academic editing is not just grammar correction. High-level support can improve response logic, sharpen manuscript positioning, align rebuttal tone with journal expectations, and make your revision easier for editors to evaluate. This is especially valuable for PhD scholars, multilingual researchers, and authors submitting to selective journals. A polished reviewer response can save a strong paper from unnecessary rejection. It can also reduce revision cycles by resolving concerns more cleanly. If your manuscript already has solid data but the narrative, structure, and rebuttal framing need work, then professional research paper assistance can be a strategic investment rather than a cosmetic one.

Final Takeaway: The Smartest Reply Is Balanced, Not Defensive

So, what is the best way to respond to a reviewer who asked for some more comparisons when there are already enough rival algorithms in the paper? The best response is balanced. Do not reject the comment bluntly. Do not expand the paper endlessly. Instead, identify the concern behind the request, clarify your baseline logic, add focused improvements where they matter, and explain respectfully why further comparisons may not be scientifically necessary or fair.

That approach aligns with what major academic publishers and style authorities expect from revision practice: point-by-point engagement, reasoned explanation, and visible manuscript improvement. (Springer Nature)

For researchers, this is more than a reviewer-response tactic. It is a publication skill. It protects scope, strengthens argument quality, and improves editorial trust. If you need expert support refining your rebuttal letter, clarifying your comparative design, or polishing your manuscript for submission, explore ContentXprtz’s PhD and academic services and writing and publishing support.

At ContentXprtz, we don’t just edit – we help your ideas reach their fullest potential.