MoSEs：基于条件阈值的文体专家混合模型的不确定性感知AI生成文本检测
MoSEs: Uncertainty-Aware AI-Generated Text Detection via Mixture of Stylistics Experts with Conditional Thresholds
AI总结：
本文提出MoSEs框架，通过文体参考库、文体感知路由器和条件阈值估计器实现文体感知的不确定性量化，在AI生成文本检测中平均提升11.34%，低资源场景提升39.15%。
摘要：
大型语言模型的快速发展加剧了公众对其潜在滥用的担忧。因此，构建可信赖的AI生成文本检测系统至关重要。现有方法忽视了文体建模，且大多依赖静态阈值，这极大地限制了检测性能。在本文中，我们提出了文体专家混合（MoSEs）框架，通过条件阈值估计实现文体感知的不确定性量化。MoSEs包含三个核心组件，即文体参考库（SRR）、文体感知路由器（SAR）和条件阈值估计器（CTE）。对于输入文本，SRR可以激活SRR中适当的参考数据并将其提供给CTE。随后，CTE联合建模语言统计属性和语义特征，以动态确定最优阈值。通过判别分数，MoSEs生成带有相应置信水平的预测标签。我们的框架在检测性能上相比基线平均提高了11.34%。更令人鼓舞的是，MoSEs在低资源情况下显示出更显著的提升，达到39.15%。我们的代码可在https://github.com/creator-xi/MoSEs获取。
英文摘要：
The rapid advancement of large language models has intensified public concerns about the potential misuse. Therefore, it is important to build trustworthy AI-generated text detection systems. Existing methods neglect stylistic modeling and mostly rely on static thresholds, which greatly limits the detection performance. In this paper, we propose the Mixture of Stylistic Experts (MoSEs) framework that enables stylistics-aware uncertainty quantification through conditional threshold estimation. MoSEs contain three core components, namely, the Stylistics Reference Repository (SRR), the Stylistics-Aware Router (SAR), and the Conditional Threshold Estimator (CTE). For input text, SRR can activate the appropriate reference data in SRR and provide them to CTE. Subsequently, CTE jointly models the linguistic statistical properties and semantic features to dynamically determine the optimal threshold. With a discrimination score, MoSEs yields prediction labels with the corresponding confidence level. Our framework achieves an average improvement 11.34% in detection performance compared to baselines. More inspiringly, MoSEs shows a more evident improvement 39.15% in the low-resource case. Our code is available at https://github.com/creator-xi/MoSEs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
