Improving LLM-based Log Parsing by Learning from Errors in Reasoning Traces
This program is tentative and subject to change.
Recent advances in reasoning-capable large language 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.
This program is tentative and subject to change.
Mon 17 NovDisplayed time zone: Seoul change
11:00 - 12:40 | |||
11:00 10mTalk | LogMoE: Lightweight Expert Mixture for Cross-System Log Anomaly Detection Research Papers Jiaxing Qi Beihang University, Zhongzhi Luan Beihang University, Shaohan Huang Beihang University, Carol Fung Concordia University, Yuchen Wang Beihang University, Aibin Wang Beihang University, Hongyu Zhang Chongqing University, Hailong Yang Beihang University, China, Depei Qian Beihang University, China | ||
11:10 10mTalk | Improving LLM-based Log Parsing by Learning from Errors in Reasoning Traces Research Papers Wang Jialai National University of Singapore, Juncheng Lu Southeast University, Jie Yang Wuhan University, Junjie Wang Institute of Software at Chinese Academy of Sciences, Zeyu Gao Tsinghua University, Chao Zhang Tsinghua University, Zhenkai Liang NUS, Ee-Chien Chang School of Computing, NUS | ||
11:20 10mTalk | LogUpdater: Automated Detection and Repair of Specific Defects in Logging Statements Journal-First Track Renyi Zhong The Chinese University of Hong Kong, Yichen LI ByteDance, Jinxi Kuang The Chinese University of Hong Kong, Wenwei Gu The Chinese University of Hong Kong, Yintong Huo Singapore Management University, Singapore, Michael Lyu The Chinese University of Hong Kong | ||
11:30 10mTalk | LogAction: Consistent Cross-system Anomaly Detection through Logs via Active Domain Adaptation Research Papers Chiming Duan Peking University, Minghua He Peking University, Pei Xiao Peking University, Tong Jia Institute for Artificial Intelligence, Peking University, Beijing, China, Xin Zhang Peking University, Zhewei Zhong Bytedance, Xiang Luo Bytedance, Yan Niu Bytedance, Lingzhe Zhang Peking University, China, Yifan Wu Peking University, Siyu Yu The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Weijie Hong Peking university, Ying Li School of Software and Microelectronics, Peking University, Beijing, China, Gang Huang Peking University | ||
11:40 10mTalk | Diplomatist: What Do Cross-language Dependencies Reflect Software Ecosystem Health? Research Papers Fanyi Meng Shenyang University of Technology, Ying Wang Northeastern University, Chun Yong Chong Monash University Malaysia, Hai Yu Northeastern University, China, Zhiliang Zhu Northeastern University, China | ||
11:50 10mTalk | Defects4Log: Benchmarking LLMs for Logging Code Defect Detection and Reasoning Research Papers Xin Wang Changsha University of Science and Technology, Zhenhao Li York University, Zishuo Ding The Hong Kong University of Science and Technology (Guangzhou) | ||
12:00 10mTalk | Which Is Better For Reducing Outdated And Vulnerable Dependencies: Pinning Or Floating? Research Papers Imranur Rahman North Carolina State University, Jill Marley North Carolina State University, William Enck North Carolina State University, Laurie Williams North Carolina State University | ||
12:10 10mTalk | On Automating Configuration Dependency Validation via Retrieval-Augmented Generation Research Papers Sebastian Simon Leipzig University, Alina Mailach Leipzig University, Johannes Dorn Leipzig University, Norbert Siegmund Leipzig University Pre-print | ||
12:20 10mTalk | CollaborLog: Efficient-Generalizable Log Anomaly Detection via Large-Small Model Collaboration in Software Evolution Research Papers Pei Xiao Peking University, Chiming Duan Peking University, Minghua He Peking University, Tong Jia Institute for Artificial Intelligence, Peking University, Beijing, China, Yifan Wu Peking University, Jing Xu ByteDance, Gege Gao ByteDance, Lingzhe Zhang Peking University, China, Weijie Hong Peking university, Ying Li School of Software and Microelectronics, Peking University, Beijing, China, Gang Huang Peking University | ||
12:30 10mTalk | On the Robustness Evaluation of 3D Obstacle Detection Against Specifications in Autonomous Driving Research Papers Tri Minh-Triet Pham Concordia University, Bo Yang Concordia University, Jinqiu Yang Concordia University | ||