Recent development of large language models (LLMs), such as ChatGPT has been widely applied to a wide range of software engineering tasks. Many papers have reported their analysis on the potential advantages and limitations of ChatGPT for writing code, summarization, text generation, etc. However, the analysis of the current state of ChatGPT for log processing has received little attention. Logs generated by large-scale software systems are complex and hard to understand. Despite their complexity, they provide crucial information for subject matter experts to understand the system status and diagnose problems of the systems. In this paper, we investigate the current capabilities of ChatGPT to perform several interesting tasks on log data, while also trying to identify its main shortcomings. Our findings show that the performance of the current version of ChatGPT for log processing is limited, with a lack of consistency in responses and scalability issues. We also outline our views on how we perceive the role of LLMs in the log processing discipline and possible next steps to improve the current capabilities of ChatGPT and the future LLMs in this area. We believe our work can contribute to future academic research to address the identified issues.

翻译：近期，诸如ChatGPT等大型语言模型（LLMs）的发展已广泛应用于各类软件工程任务。许多论文报告了ChatGPT在代码编写、文本摘要、内容生成等方面的潜在优势与局限性分析。然而，针对ChatGPT当前处理日志数据能力的评估研究仍相对匮乏。大规模软件系统生成的日志复杂且难以理解，尽管存在复杂性，它们为领域专家理解系统状态和诊断系统问题提供了关键信息。本文研究了ChatGPT在日志数据上执行若干有趣任务的当前能力，同时试图识别其主要缺陷。研究结果表明，当前版本的ChatGPT在日志处理方面的性能有限，存在响应不一致和可扩展性问题。我们还阐述了如何看待LLMs在日志处理领域中的作用，以及改进ChatGPT及未来LLMs当前能力的可能后续步骤。我们相信，本工作可为未来针对上述问题的学术研究提供参考。