计算机工程2026，Vol.52Issue(8)：1-15,15.DOI:10.19678/j.issn.1000-3428.0260211
基于大语言模型的日志异常检测方法综述
Survey on Log Anomaly Detection Methods Based on Large Language Models
摘要
Abstract
As the scale and complexity of complex intelligent systems represented by high-performance computing and embedded systems continue to grow,logs,as core operational data,have become critical for ensuring reliable system operation through automated anomaly detection.Traditional log anomaly detection methods driven by machine learning and deep learning primarily focus mostly on log sequence modeling but suffer from insufficient semantic understanding and limited generalization ability.Large Language Models(LLMs),with their superior semantic understanding and contextual reasoning capabilities,have effectively overcome this limitation.Since the advent of LLM technology,relevant research has emerged rapidly,achievements are scattered across multiple technical paths and lack a systematic review.This paper provides a comprehensive survey of log anomaly detection methods based on LLMs.We select 35 core papers and establish a unified technical classification framework,categorizing existing methods into five technical routes:prompt engineering,Retrieval-Augmented Generation(RAG),domain fine-tuning,Reinforcement Learning(RL),and large-small model collaboration.Our analysis reveals that Supervised Fine-Tuning(SFT)is currently the most widely adopted technical route,while the large-small model collaborative architecture,as an emerging paradigm,is shifting research focus from merely pursuing detection accuracy to balancing inference efficiency and industrial deployability.The current evaluation system remains heavily focused on detection performance metrics,with insufficient attention paid to efficiency overhead and interpretability.Finally,this paper identifies inference latency bottlenecks and data privacy challenges of LLMs when processing ultra-long and massive log streams,and provides insights into frontier directions such as lightweight deployment and online continual learning.关键词
日志异常检测/大语言模型/软件运维/提示工程/微调/检索增强生成Key words
log anomaly detection/Large Language Model(LLM)/software operation and maintenance/prompt engineering/fine-tuning/Retrieval-Augmented Generation(RAG)分类
信息技术与安全科学引用本文复制引用
陈昕,孙溢成,谈诚..基于大语言模型的日志异常检测方法综述[J].计算机工程,2026,52(8):1-15,15.基金项目
重庆市自然科学基金(cstc2021jcyj-msxmX1115) （cstc2021jcyj-msxmX1115）
国家级大学生创新创业训练计划(S202510497187). （S202510497187）
