用4比特编码提升日志解析精度，兼顾高效与细节捕捉。
A Word is Worth 4-bit: Efficient Log Parsing with Binary Coded Decimal Recognition
- 通过字符级嵌入+二进制编码解码，实现细粒度日志模板提取。
- 在Loghub-2k和工业数据集上，准确率媲美大模型，效率更高。
- 适合资源受限场景下的高精度日志分析，尤其适合运维监控。
系统生成的日志通常通过解析转换为分类日志模板，这些模板对下游任务中的可操作洞察至关重要。然而，现有解析器常无法捕捉细粒度模板特征，导致准确率不足，影响下游任务中精确模式识别的实用性。本文提出一种基于新型神经架构的字符级日志解析方法，通过聚合字符嵌入并估计一连串二进制编码十进制数，实现高粒度日志模板提取。该低资源字符级解析器在修订版Loghub-2k及人工标注的工业数据集上测试，准确率与基于大语言模型的解析器相当，同时在效率上优于语义解析器。
原文摘要 · Abstract (English)
System-generated logs are typically converted into categorical log templates through parsing. These templates are crucial for generating actionable insights in various downstream tasks. However, existing parsers often fail to capture fine-grained template details, leading to suboptimal accuracy and reduced utility in downstream tasks requiring precise pattern identification. We propose a character-level log parser utilizing a novel neural architecture that aggregates character embeddings. Our approach estimates a sequence of binary-coded decimals to achieve highly granular log templates extraction. Our low-resource character-level parser, tested on revised Loghub-2k and a manually annotated industrial dataset, matches LLM-based parsers in accuracy while outperforming semantic parsers in efficiency.
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