Reproduce “Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies” on OpenResearch

About this paper

Yilun Liu, Shimin Tao, Weibin Meng, Jingyu Wang, Wenbing Ma, Yanqing Zhao, Yuhang Chen, Hao Yang and 2 more

LogPrompt, developed by Huawei and university researchers, employs Large Language Models with sophisticated prompt strategies to deliver interpretable log parsing and anomaly detection. The approach achieves superior performance in online scenarios, outperforming existing methods by up to 55.9% F1-score for anomaly detection and 37.4% for parsing without requiring in-domain training, while generating highly rated human-readable explanations.

Key insights

• Sophisticated prompt engineering, including self-prompting and Chain-of-Thought strategies, is crucial to effectively unlock and significantly enhance LLM capabilities for complex log analysis tasks, improving performance by up to 380.7% over simple prompts.
• Requiring LLMs to provide justifications for their predictions (Implicit CoT) compels the models to activate more logical and coherent thought processes, leading to more accurate and interpretable results.
• LogPrompt's method demonstrates compatibility with smaller, open-source LLMs like Vicuna, making advanced LLM-based log analysis more accessible and deployable in resource-constrained or privacy-sensitive industrial environments.

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