一句话：分析LLM日志解析推理轨迹中的错误类型，生成日志变体微调模型，显著提升解析准确率。
针对基于大语言模型的日志解析器，本文研究如何利用推理轨迹提升性能。提出 TraceDoctor 框架：先分析解析错误对应的推理轨迹，归纳高层错误类型；再依据这些错误类型设计有针对性的日志变体生成策略，并用生成数据微调 LLM。作者在五种具备推理能力的 SOTA LLM 上实例化日志解析器，共识别出 29 种高层错误类型。实验表明，该方法使解析准确率（PA）和分组准确率（GA）平均最高分别提升 17.3% 和 16.3%。
Recent advances in reasoning-capable large lan-guage models (LLMs) have led to their application in a wide range of tasks, including log parsing. These LLMs generate intermediate reasoning traces during inference, offering a unique opportunity to analyze and improve their performance. In this work, we investigate how reasoning traces can be leveraged to enhance LLM-based log parsers. We propose TraceDoctor, a framework that analyzes reasoning traces associated with parsing errors to understand the causes of failure. We categorize these error causes into high-level error types and design targeted log variant generation strategies guided by these high-level error types. The generated variants are then used to fine-tune the LLMs. We instantiate five state-of-the-art (SOTA) reasoning-capable LLMs as log parsers and identify 29 distinct high-level error types. Our approach improves their average parsing accuracy by up to 17.3% and 16.3% on parsing accuracy (PA) and group accuracy (GA), respectively.
DOI 原文 ·
@inproceedings{paperbot9192,
title = {Improving LLM-based Log Parsing by Learning from Errors in Reasoning Traces},
author = {Jialai Wang and Juncheng Lu and Jie Yang and Junjie Wang and Zeyu Gao and Chao Zhang and Zhenkai Liang and Ee-Chien Chang},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00066}
}