发现学术审稿中AI生成内容快速上升，2025年超1/5审稿疑似由AI辅助。
Detecting AI-Generated Content in Academic Peer Reviews
- 用历史审稿训练检测模型，追踪ICLR与Nature Communications的审稿变化。
- 2025年ICLR审稿中约20%、NC审稿中12%被判定为AI生成。
- 自然期刊审稿中AI内容增长最显著时段在2024年三季度至四季度。
大规模语言模型（LLMs）的普及引发了对其在学术同行评审中角色的担忧。本研究通过将基于历史审稿训练的检测模型应用于国际学习表征会议（ICLR）和《自然·通讯》（Nature Communications）后续评审周期，分析了审稿中AI生成内容的时间演变。结果显示，2022年前几乎未检测到AI生成内容，此后迅速上升，至2025年，约20%的ICLR审稿和12%的《自然·通讯》审稿被识别为AI生成。其中，《自然·通讯》的AI审稿增长最明显发生在2024年第三季度至第四季度。这些发现表明AI辅助审稿正迅速增多，亟需深入研究其对学术评价的影响。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。