Out-of-distribution (OOD) detection, which maps high-dimensional data into a scalar OOD score, is critical for the reliable deployment of machine learning models. A key challenge in recent research is how to effectively leverage and aggregate token embeddings from language models to obtain the OOD score. In this work, we propose AP-OOD, a novel OOD detection method for natural language that goes beyond simple average-based aggregation by exploiting token-level information. AP-OOD is a semi-supervised approach that flexibly interpolates between unsupervised and supervised settings, enabling the use of limited auxiliary outlier data. Empirically, AP-OOD sets a new state of the art in OOD detection for text: in the unsupervised setting, it reduces the FPR95 (false positive rate at 95% true positives) from 27.84% to 4.67% on XSUM summarization, and from 77.08% to 70.37% on WMT15 En-Fr translation.

翻译：分布外（OOD）检测将高维数据映射为一个标量OOD分数，对于机器学习模型的可靠部署至关重要。近期研究的一个关键挑战是如何有效利用并聚合来自语言模型的词元嵌入以获得OOD分数。在本工作中，我们提出了AP-OOD，一种新颖的自然语言OOD检测方法，它通过利用词元级信息，超越了简单的基于平均的聚合方式。AP-OOD是一种半监督方法，可在无监督与有监督设置之间灵活插值，从而能够利用有限的辅助离群数据。经验表明，AP-OOD在文本OOD检测中达到了新的最优水平：在无监督设置下，其在XSUM摘要任务上将FPR95（真阳性率为95%时的假阳性率）从27.84%降至4.67%，在WMT15 En-Fr翻译任务上从77.08%降至70.37%。