Detecting AI-Generated Content in Academic Peer Reviews
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Abstract (arXiv)
The growing availability of large language models (LLMs) has raised questions about their role in academic peer review. This study examines the temporal emergence of AI-generated content in peer reviews by applying a detection model trained on historical reviews to later review cycles at International Conference on Learning Representations (ICLR) and Nature Communications (NC). We observe minimal detection of AI-generated content before 2022, followed by a substantial increase through 2025, with approximately 20% of ICLR reviews and 12% of Nature Communications reviews classified as AI-generated in 2025. The most pronounced growth of AI-generated reviews in NC occurs between the third and fourth quarter of 2024. Together, these findings provide suggestive evidence of a rapidly increasing presence of AI-assisted content in peer review and highlight the need for further study of its implications for scholarly evaluation.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 2 of its 7 references that are in the map. First posted on arXiv 30 Jan 2026.
Builds on
2 of 7 references.
References · 2
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2020Longformer: The Long-Document Transformercited by 3,413
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Citing papers per year: 2026: 4.
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