LogLM: From Task-based to Instruction-based Automated Log Analysis

• URL: http://arxiv.org/abs/2410.09352v2
• Date: Thu, 09 Jan 2025 09:24:40 GMT
• Title: LogLM: From Task-based to Instruction-based Automated Log Analysis
• Authors: Yilun Liu, Yuhe Ji, Shimin Tao, Minggui He, Weibin Meng, Shenglin Zhang, Yongqian Sun, Yuming Xie, Boxing Chen, Hao Yang,
• Abstract summary: Existing approaches mostly treat log analysis as training a model to perform an isolated task. We propose an instruction-based training approach that transforms log-label pairs into a unified format of instruction-response pairs. Our trained model, LogLM, can follow complex user instructions and generalize better across different tasks.
• Score: 22.44842963552044
• License:
• Abstract: Automatic log analysis is essential for the efficient Operation and Maintenance (O&M) of software systems, providing critical insights into system behaviors. However, existing approaches mostly treat log analysis as training a model to perform an isolated task ( e.g., anomaly detection, log parsing, etc.) using task-specific log-label pairs. These task-based approaches are inflexible in generalizing to complex scenarios, depend on task-specific training data, and cost significantly when deploying multiple models. In this paper, we propose an instruction-based training approach that transforms log-label pairs from multiple tasks and domains into a unified format of instruction-response pairs. Our trained model, LogLM, can follow complex user instructions and generalize better across different tasks, thereby increasing flexibility and reducing the dependence on task-specific training data. By integrating major log analysis tasks into a single model, our approach also relieves model deployment burden. Experimentally, LogLM outperforms existing approaches across five log analysis capabilities, and exhibits strong generalization abilities on complex instructions and unseen tasks.

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