Sem-Detect: Semantic Level Detection of AI Generated Peer-Reviews
A new method, Sem-Detect, distinguishes human-written peer reviews from AI-generated ones by combining textual features with claim-level semantic analysis. It leverages the observation that different AI models converge on similar points while human reviewers are more diverse. On over 20,000 reviews from ICLR and NeurIPS, it improves [email protected]% FPR by 25.5% in binary setting. In three-class scenario, fewer than 3.5% of LLM-refined human reviews are misclassified as AI-generated.
[2605.21713] Sem-Detect: Semantic Level Detection of AI Generated Peer-Reviews
[Submitted on 20 May 2026]
Title:Sem-Detect: Semantic Level Detection of AI Generated Peer-Reviews
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Abstract:How can we distinguish whether a peer review was written by a human or generated by an AI model? We argue that, in this setting, authorship should not be attributed solely from the textual features of a review, but also from the ideas, judgments, and claims it expresses. To this end, we propose Sem-Detect, an authorship detection method for peer reviews that operationalizes this principle by combining textual features with claim-level semantic analysis. Sem-Detect compares a target review against multiple AI-generated reviews of the same paper, leveraging the observation that different AI models tend to converge on similar points, while human reviewers introduce more unique and diverse ones. As a result, Sem-Detect is able to distinguish fully AI reviews from authentic human-written ones, including those that have been refined using an LLM but still reflect human judgment. Across a dataset of over 20,000 peer reviews from ICLR and NeurIPS conferences, Sem-Detect improves over the strongest baseline by 25.5% in [email protected]% FPR in the binary setting. Moreover, in the three-class scenario, we empirically show that LLM refinement preserves the semantic signals of human reviews, which remain distinct from the patterns exhibited by fully AI-generated text; as a result, fewer than 3.5% of LLM-refined human reviews are misclassified as AI-generated.
Subjects:
Computation and Language (cs.CL)
ACM classes: I.2
Cite as: arXiv:2605.21713 [cs.CL]
(or arXiv:2605.21713v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.21713
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: André Duarte [view email] [v1] Wed, 20 May 2026 20:19:16 UTC (911 KB)
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