检测学术审稿是否由AI生成，守护科研诚信
'Quis custodiet ipsos custodes?' Who will watch the watchmen? On Detecting AI-generated peer-reviews
- 基于词频重复和重提示再生原理，构建双模型检测AI审稿
- 在对抗攻击下仍保持高准确率，尤其重提示模型更稳健
- 适合期刊编辑、学术管理者用于防范AI滥用
学术同行评审的可靠性对维护科研严谨性至关重要。随着大型语言模型（如ChatGPT）在学术写作中的广泛应用，其生成文本可能威胁科学出版，包括同行评审环节。现有研究多聚焦于通用AI文本检测或估算AI审稿占比，而本文旨在解决实际问题：帮助编辑判断某篇评审是否由ChatGPT生成。为此，提出两项新方法：基于词频重复的TF模型，以及基于重提示再生一致性的RR模型。在令牌攻击与改写攻击下进行压力测试，并提出有效防御策略以缓解改写影响。实验表明，所提方法优于其他主流检测器；其中RR模型更具鲁棒性，而TF模型在无攻击时表现更佳。代码、数据集与模型均已开源。
原文摘要 · Abstract (English)
The integrity of the peer-review process is vital for maintaining scientific rigor and trust within the academic community. With the steady increase in the usage of large language models (LLMs) like ChatGPT in academic writing, there is a growing concern that AI-generated texts could compromise scientific publishing, including peer-reviews. Previous works have focused on generic AI-generated text detection or have presented an approach for estimating the fraction of peer-reviews that can be AI-generated. Our focus here is to solve a real-world problem by assisting the editor or chair in determining whether a review is written by ChatGPT or not. To address this, we introduce the Term Frequency (TF) model, which posits that AI often repeats tokens, and the Review Regeneration (RR) model, which is based on the idea that ChatGPT generates similar outputs upon re-prompting. We stress test these detectors against token attack and paraphrasing. Finally, we propose an effective defensive strategy to reduce the effect of paraphrasing on our models. Our findings suggest both our proposed methods perform better than the other AI text detectors. Our RR model is more robust, although our TF model performs better than the RR model without any attacks. We make our code, dataset, and model public.
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