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CoLA: Model Collaboration for Log-based Anomaly Detection

delete2025-07-01
delete0
PRE
AI
Z
Zhu, Xuhang
T
Tang Xiu *
S
Sai Wu
J
Jichen Li
L
Li, Chenxing
Q
Quanqing Xu
陈
陈刚 (Gang Chen)
DOI：10.14778/3749646.3749668delete
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摘要

摘要

En 中文
基于日志的异常检测在确保系统可靠性方面发挥着关键作用。尽管基于深度学习的小型检测模型（SDMs）具有高效性，但大型语言模型（LLMs）则更为精确，并且能够提供解释。直观地看，一个引人深思的问题由此产生：我们能否无缝融合这两种方法的优势？在本项工作中，我们深入探讨了这一尚未充分研究的方向，并提出了一种新型协同日志异常检测框架CoLA。在协同推理过程中，SDM充当过滤器以筛选潜在的异常实例，而下游的LLM则作为专家负责检测异常、提供解释并优化SDM。在三个大型真实世界数据集上的广泛实验表明，CoLA在有效性、效率和可解释性方面显著优于当前最先进的方法，同时还能大幅降低人力成本。
Keyword:
LANGUAGE MODELS

期刊

P
Proceedings of the VLDB Endowment
IF:
3.3
论文数:
563
被引数:
1.2W

机构

A
Ant Group
学者数:
23
论文数: 10
被引数: 0
Z
zhejiang university
学者数:
17.7W
论文数: 12.1W
被引数: 152
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