Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging

2024-02-28

arXiv.org

AI Summary

Existing log parsing methods rely heavily on handcrafted rules and statistical features, neglecting semantic information—leading to inaccurate template matching and poor generalization. To address this, we propose a structured template generation framework that synergistically integrates entropy-driven log clustering with large language model (LLM)-based chain-of-thought reasoning. Specifically, we introduce an information-entropy-guided automatic sampling strategy to replace manual rule design, and develop a semantics-aware chain-of-thought template merging mechanism that deeply embeds LLM inference capabilities into the entire template induction pipeline. Evaluated on multiple large-scale public benchmarks, our method achieves state-of-the-art performance, significantly improving parameter identification accuracy and template generalizability across diverse log formats. The implementation is publicly available.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Statistical Relational/Logic Learning
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• 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

Abstract

Logs produced by extensive software systems are integral to monitoring system behaviors. Advanced log analysis facilitates the detection, alerting, and diagnosis of system faults. Log parsing, which entails transforming raw log messages into structured templates, constitutes a critical phase in the automation of log analytics. Existing log parsers fail to identify the correct templates due to reliance on human-made rules. Besides, These methods focus on statistical features while ignoring semantic information in log messages. To address these challenges, we introduce a cutting-edge extbf{L}og parsing framework with extbf{E}ntropy sampling and Chain-of-Thought extbf{M}erging (Lemur). Specifically, to discard the tedious manual rules. We propose a novel sampling method inspired by information entropy, which efficiently clusters typical logs. Furthermore, to enhance the merging of log templates, we design a chain-of-thought method for large language models (LLMs). LLMs exhibit exceptional semantic comprehension, deftly distinguishing between parameters and invariant tokens. We have conducted experiments on large-scale public datasets. Extensive evaluation demonstrates that Lemur achieves the state-of-the-art performance and impressive efficiency. The Code is available at https://github.com/zwpride/lemur.

Problem

• Log Parsing
• Accuracy Improvement
• Complex Log Understanding

Innovation

• Entropy Sampling
• Chain-of-Thought Consolidation
• Intelligent Logging Parsing