  Log parsing serves as an essential prerequisite for various log analysis
tasks. Recent advancements in this field have improved parsing accuracy by
leveraging the semantics in logs through fine-tuning large language models
(LLMs) or learning from in-context demonstrations. However, these methods
heavily depend on labeled examples to achieve optimal performance. In practice,
collecting sufficient labeled data is challenging due to the large scale and
continuous evolution of logs, leading to performance degradation of existing
log parsers after deployment. To address this issue, we propose LUNAR, an
unsupervised LLM-based method for efficient and off-the-shelf log parsing. Our
key insight is that while LLMs may struggle with direct log parsing, their
performance can be significantly enhanced through comparative analysis across
multiple logs that differ only in their parameter parts. We refer to such
groups of logs as Log Contrastive Units (LCUs). Given the vast volume of logs,
obtaining LCUs is difficult. Therefore, LUNAR introduces a hybrid ranking
scheme to effectively search for LCUs by jointly considering the commonality
and variability among logs. Additionally, LUNAR crafts a novel parsing prompt
for LLMs to identify contrastive patterns and extract meaningful log structures
from LCUs. Experiments on large-scale public datasets demonstrate that LUNAR
significantly outperforms state-of-the-art log parsers in terms of accuracy and
efficiency, providing an effective and scalable solution for real-world
deployment. \footnote{The code and data are available at
\url{https://github.com/Jun-jie-Huang/LUNAR}}.
