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This study analyzes variations in peer review datasets—specifically paper versions, scoring methods, and input formats—to determine their impact on downstream machine learning tasks. The authors provide actionable best practices for data providers and users to ensure higher fidelity when training models on academic literature.

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What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

From peer review nuances to best practices

arXivJuly 28, 20261 min read

arXiv:2607.22681v1 Announce Type: cross Abstract: This report studies three nuances in peer review data: paper version, score version, and input format. We characterize how the variants differ, and measure their impact on downstream tasks. Based on our findings, we offer best practices for both data providers and data users.

Abstract:This report studies three nuances in peer review data: paper version, score version, and input format. We characterize how the variants differ, and measure their impact on downstream tasks. Based on our findings, we offer best practices for both data providers and data users.

Digital Libraries (cs.DL); Computation and Language (cs.CL)

arXiv:2607.22681 [cs.DL]

(or arXiv:2607.22681v1 [cs.DL] for this version)

https://doi.org/10.48550/arXiv.2607.22681 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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