用多个语言模型增强文本异常检测，提升跨数据集适应性。
Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representations
- 融合多个预训练模型的嵌入表示，从多视角建模正常文本模式。
- 在10个基准数据集上优于主流方法，异常检测准确率显著提升。
- 适合需要高鲁棒性的内容审核、垃圾信息过滤等场景。
文本异常检测（TAD）在有害内容治理、网络钓鱼识别和垃圾评论过滤等语言驱动应用中至关重要。尽管两阶段‘嵌入-检测’方法已达到先进水平，但其性能常受限于单一嵌入模型的使用以及在不同数据集和异常类型间的适应性不足。为此，本文提出利用多个预训练语言模型的嵌入表示，构建MCA²多视角文本异常检测框架。MCA²采用多视角重建模型，从多个嵌入视角有效提取正常文本模式；设计对比协作模块以挖掘各视角间的互补性并强化交互；引入自适应分配模块，自动调整各视角贡献权重，增强对多样化数据集的适应能力。在10个基准数据集上的大量实验验证了MCA²相对于强基线方法的有效性。代码已开源：https://github.com/yankehan/MCA2。
原文摘要 · Abstract (English)
Text anomaly detection (TAD) plays a critical role in various language-driven real-world applications, including harmful content moderation, phishing detection, and spam review filtering. While two-step "embedding-detector" TAD methods have shown state-of-the-art performance, their effectiveness is often limited by the use of a single embedding model and the lack of adaptability across diverse datasets and anomaly types. To address these limitations, we propose to exploit the embeddings from multiple pretrained language models and integrate them into $MCA^2$, a multi-view TAD framework. $MCA^2$ adopts a multi-view reconstruction model to effectively extract normal textual patterns from multiple embedding perspectives. To exploit inter-view complementarity, a contrastive collaboration module is designed to leverage and strengthen the interactions across different views. Moreover, an adaptive allocation module is developed to automatically assign the contribution weight of each view, thereby improving the adaptability to diverse datasets. Extensive experiments on 10 benchmark datasets verify the effectiveness of $MCA^2$ against strong baselines. The source code of $MCA^2$ is available at https://github.com/yankehan/MCA2.
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