Sem-Detect: Semantic Level Detection of AI Generated Peer-Reviews
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 on...
| Authors | André V. Duarte, Brian Tufts, Aditya Oke et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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