mlsanityData to 14 Oct 2026

Detecting AI-Generated Content in Academic Peer Reviews

arXiv 2602.00319 ↗First posted 30 Jan 2026cs.CL cs.AI cs.LGcited by 4

Not on the map. Why?

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

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2020Longformer: The Long-Document Transformercited by 3,413

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.