LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection

2023/09/03 by Jiaxing Qi, Qi, Jiaxing, Shaohan Huang +9 · 14 citations
Computer Science · Decision Sciences · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Data Quality and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Software Engineering (cs.SE) #Software System Performance and Reliability

paper · pdf · doi:10.48550/arxiv.2309.01189

openalex publication_date 2023/09/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-based anomaly detection. These methods face several challenges such as high-dimensional and noisy log data, class imbalance, generalization, and model interpretability. Recently, ChatGPT has shown promising results in various domains. However, there is still a lack of study on the application of ChatGPT for log-based anomaly detection. In this work, we proposed LogGPT, a log-based anomaly detection framework based on ChatGPT. By leveraging the ChatGPT's language interpretation capabilities, LogGPT aims to explore the transferability of knowledge from large-scale corpora to log-based anomaly detection. We conduct experiments to evaluate the performance of LogGPT and compare it with three deep learning-based methods on BGL and Spirit datasets. LogGPT shows promising results and has good interpretability. This study provides preliminary insights into prompt-based models, such as ChatGPT, for the log-based anomaly detection task.

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