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experimental fairness

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How to Respond When Reviewers Claim Your Experimental Setup Is Unfair

The article outlines why reviewers question experimental fairness, identifies common concerns such as data splits, training epochs, backbone consistency, random seeds, hyper‑parameter search, and compute budget, and provides concrete examples of ineffective and effective rebuttal phrasing to demonstrate a thorough, verifiable response.

NLPacademic writingexperimental fairness
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How to Respond When Reviewers Claim Your Experimental Setup Is Unfair