AI for IT Operations (AIOps) is a powerful platform that Site Reliability Engineers (SREs) use to automate and streamline operational workflows with minimal human intervention. Automated log analysis is a critical task in AIOps as it provides key insights for SREs to identify and address ongoing faults. Tasks such as log format detection, log classification, and log parsing are key components of automated log analysis. Most of these tasks require supervised learning; however, there are multiple challenges due to limited labelled log data and the diverse nature of log data. Large Language Models (LLMs) such as BERT and GPT3 are trained using self-supervision on a vast amount of unlabeled data. These models provide generalized representations that can be effectively used for various downstream tasks with limited labelled data. Motivated by the success of LLMs in specific domains like science and biology, this paper introduces a LLM for log data which is trained on public and proprietary log data. The results of our experiments demonstrate that the proposed LLM outperforms existing models on multiple downstream tasks. In summary, AIOps powered by LLMs offers an efficient and effective solution for automating log analysis tasks and enabling SREs to focus on higher-level tasks. Our proposed LLM, trained on public and proprietary log data, offers superior performance on multiple downstream tasks, making it a valuable addition to the AIOps platform.

翻译：人工智能运维（AIOps）是一个强大的平台，站点可靠性工程师（SREs）利用它实现运维工作流的自动化与精简，显著降低人工干预。自动化日志分析是AIOps中的关键任务，因为它能为SREs识别与处理正在发生的故障提供重要洞察。日志格式检测、日志分类与日志解析等任务构成了自动化日志分析的核心环节。这些任务大多依赖监督学习，然而，由于标注日志数据的稀缺性以及日志数据的多样性，存在诸多挑战。像BERT和GPT3这样的大型语言模型（LLMs）通过在海量未标注数据上进行自监督训练而得到。这些模型学习到的泛化表示能够有效应用于标注数据有限的各类下游任务。受LLMs在科学与生物学等特定领域取得成功的启发，本文提出一种面向日志数据的LLM，该模型在公有和私有日志数据上进行了训练。实验结果表明，所提出的LLM在多项下游任务上优于现有模型。综上所述，基于LLM的AIOps为日志分析任务的自动化提供了一种高效且有效的解决方案，使SREs能够专注于更高层次的任务。我们所提出的LLM在公有和私有日志数据上训练后，在多项下游任务上展现出优越性能，从而成为AIOps平台的一个宝贵补充。