Few-Shot Log Analysis with Prompt-Based Multi-task Transfer Learning

Mingjie Zhou, Weidong Yang, Lipeng Ma, Sihang Jiang, Bo Xu, Yanghua Xiao

Abstract: Log analysis is critical to software system operations and maintenance. Sparse annotated log data can reduce the performance of mainstream automated log analysis methods. Fortunately, the various types of tasks in log analysis can mutually promote performance. we argue that leveraging knowledge learned from source log analysis tasks can improve the performance of target log analysis tasks in few-shot scenarios. In this paper, we propose LogMT, a two-stage method that leverages deep prompt tuning to learn log analysis knowledge from multiple source tasks and transfers it to few-shot target tasks by a mixture-of-experts (MoE) router. To evaluate LogMT’s performance, we conduct nine few-shot log analysis experiments, each of them consisting of eight source log analysis tasks and one few-shot target task. The results demonstrate that LogMT achieves state-of-the-art performance on nine few-shot log analysis experiments. The source code of this paper can be found in https://github.com/nonauthor/LogMT.