Detecting anomalies or out-of-distribution (OOD) samples is critical for maintaining the reliability and trustworthiness of machine learning systems. Recently, Large Language Models (LLMs) have demonstrated their effectiveness not only in natural language processing but also in broader applications due to their advanced comprehension and generative capabilities. The integration of LLMs into anomaly and OOD detection marks a significant shift from the traditional paradigm in the field. This survey focuses on the problem of anomaly and OOD detection under the context of LLMs. We propose a new taxonomy to categorize existing approaches into three classes based on the role played by LLMs. Following our proposed taxonomy, we further discuss the related work under each of the categories and finally discuss potential challenges and directions for future research in this field. We also provide an up-to-date reading list of relevant papers.

本研究解决了机器学习系统中异常和分布外样本检测的重要性及其现存方法的不足。通过提出一种新的分类法，研究将现有方法根据大型语言模型的作用进行分类，并探讨了未来研究的挑战与方向。这项工作的显著发现是，大型语言模型在异常和分布外检测中的整合可能会显著改变该领域的传统范式。

大型语言模型在异常和分布外检测中的应用概述