LLM用于异常与分布外检测，新分类法梳理主流方法
Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey
- 按LLM角色分两类：辅助检测与核心决策
- 总结现有方法并构建系统性分类框架
- 适合关注大模型应用前景的研究者
异常或分布外（OOD）样本检测对保障机器学习系统的可靠性与可信度至关重要。近年来，大语言模型（LLMs）凭借其强大的理解与生成能力，在自然语言处理之外的领域也展现出显著成效。将LLMs引入异常与OOD检测，标志着该领域的范式转变。本综述聚焦于LLMs背景下的异常与OOD检测问题，提出一种新分类体系，将现有方法按LLM所扮演角色分为两类。基于该分类，系统梳理各类别相关工作，并探讨该领域潜在挑战与未来研究方向。此外，还提供最新的相关论文阅读清单。
原文摘要 · Abstract (English)
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 two 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。