Sem-Detect: Semantic Level Detection of AI Generated Peer-Reviews
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Computer Science > Computation and Language
Title:Sem-Detect: Semantic Level Detection of AI Generated Peer-Reviews
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)
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