TAILOR：面向长尾日志解析的模板保持增强方法
原标题：TAILOR: Template-Preserving Augmentation for Long-Tailed Log Parsing
AI 摘要
该论文研究日志解析中的长尾分布问题，发现罕见日志组（少于5个实例）在Loghub-2.0基准中占近20%的模板但不足0.01%的日志消息，导致所有解析器在这些组上性能大幅下降。作者提出TAILOR框架，通过模板保持增强为罕见日志组补充结构一致的日志消息，从而提升模板推断准确率。实验显示TAILOR在罕见日志组上比最强基线提升19%的解析准确率，并在完整数据集上保持竞争力，且该增强策略可泛化到不同LLM骨干。
正文节选
TAILOR: Template-Preserving Augmentation for Long-Tailed Log Parsing Abstract. Log parsing is essential for system log analysis because it supports tasks such as debugging, monitoring, and anomaly detection by transforming unstructured log messages into structured log templates. However, real-world log datasets exhibit highly imbalanced, long-tailed distributions, where a small number of frequent templates dominate while many rare templates appear only a few times. This imbalance causes evaluati