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

2024-10-12

arXiv.org

AI Summary

Existing log analysis models are task-specific, rely heavily on domain-specific annotated data, exhibit poor generalization, and struggle with complex or unseen instructions. Method: We propose LogLM, an instruction-driven large language model for log analysis, which unifies diverse log tasks—including anomaly detection, parsing, and summarization—into a standardized instruction-response format. LogLM is adapted to the log domain via multi-task instruction tuning and log-specific instruction engineering. It accepts natural-language instructions and supports zero-shot cross-task transfer. Contribution/Results: Experiments demonstrate that LogLM outperforms all state-of-the-art methods across five core log analysis tasks. It exhibits strong generalization to complex instructions and previously unseen tasks. As a single unified model, LogLM replaces multiple specialized models, significantly improving deployment efficiency and task-agnostic capability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Log Analysis
• Model Adaptability
• Task Generalization

Innovation

• LogLM Model
• Unified Log Analysis
• Flexible Command Understanding