Real-world data deviating from the independent and identically distributed
(i.i.d.) assumption of in-distribution training data poses security threats to
deep networks, thus advancing out-of-distribution (OOD) detection algorithms.
Detection methods in generative language models (GLMs) mainly focus on
uncertainty estimation and embedding distance measurement, with the latter
proven to be most effective in traditional linguistic tasks like summarization
and translation. However, another complex generative scenario mathematical
reasoning poses significant challenges to embedding-based methods due to its
high-density feature of output spaces, but this feature causes larger
discrepancies in the embedding shift trajectory between different samples in
latent spaces. Hence, we propose a trajectory-based method TV score, which uses
trajectory volatility for OOD detection in mathematical reasoning. Experiments
show that our method outperforms all traditional algorithms on GLMs under
mathematical reasoning scenarios and can be extended to more applications with
high-density features in output spaces, such as multiple-choice questions.

提出了一种基于轨迹的方法 TV score，通过使用轨迹的波动性进行数学推理中的离群数据检测，实验证明该方法在数学推理场景下优于传统算法，并可扩展到输出空间具有高密度特征的更多应用，如多项选择题。