  Log analysis is crucial for ensuring the orderly and stable operation of
information systems, particularly in the field of Artificial Intelligence for
IT Operations (AIOps). Large Language Models (LLMs) have demonstrated
significant potential in natural language processing tasks. In the AIOps
domain, they excel in tasks such as anomaly detection, root cause analysis of
faults, operations and maintenance script generation, and alert information
summarization. However, the performance of current LLMs in log analysis tasks
remains inadequately validated. To address this gap, we introduce LogEval, a
comprehensive benchmark suite designed to evaluate the capabilities of LLMs in
various log analysis tasks for the first time. This benchmark covers tasks such
as log parsing, log anomaly detection, log fault diagnosis, and log
summarization. LogEval evaluates each task using 4,000 publicly available log
data entries and employs 15 different prompts for each task to ensure a
thorough and fair assessment. By rigorously evaluating leading LLMs, we
demonstrate the impact of various LLM technologies on log analysis performance,
focusing on aspects such as self-consistency and few-shot contextual learning.
We also discuss findings related to model quantification, Chinese-English
question-answering evaluation, and prompt engineering. These findings provide
insights into the strengths and weaknesses of LLMs in multilingual environments
and the effectiveness of different prompt strategies. Various evaluation
methods are employed for different tasks to accurately measure the performance
of LLMs in log analysis, ensuring a comprehensive assessment. The insights
gained from LogEvals evaluation reveal the strengths and limitations of LLMs in
log analysis tasks, providing valuable guidance for researchers and
practitioners.
