Log Parsing with Prompt-based Few-shot Learning

TL;DR

This paper introduces LogPPT, a prompt-based few-shot learning approach for log parsing that effectively captures log templates with minimal labeled data, outperforming traditional methods.

Contribution

The paper presents a novel prompt tuning method combined with adaptive sampling to improve log template recognition with few labeled examples.

Findings

LogPPT outperforms existing log parsers on 16 datasets.

It achieves high accuracy with minimal labeled data.

The approach is both effective and efficient.

Abstract

Logs generated by large-scale software systems provide crucial information for engineers to understand the system status and diagnose problems of the systems. Log parsing, which converts raw log messages into structured data, is the first step to enabling automated log analytics. Existing log parsers extract the common part as log templates using statistical features. However, these log parsers often fail to identify the correct templates and parameters because: 1) they often overlook the semantic meaning of log messages, and 2) they require domain-specific knowledge for different log datasets. To address the limitations of existing methods, in this paper, we propose LogPPT to capture the patterns of templates using prompt-based few-shot learning. LogPPT utilises a novel prompt tuning method to recognise keywords and parameters based on a few labelled log data. In addition, an adaptive random sampling algorithm is designed to select a small yet diverse training set. We have conducted extensive experiments on 16 public log datasets. The experimental results show that LogPPT is effective and efficient for log parsing.