[2409.01909] LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models

<!DOCTYPE html> <html lang="en"> <head> <title>[2409.01909] LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models</title> <meta name="viewport" content="width=device-width, initial-scale=1"> <link rel="apple-touch-icon" sizes="180x180" href="/static/browse/0.3.4/images/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="/static/browse/0.3.4/images/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="/static/browse/0.3.4/images/icons/favicon-16x16.png"> <link rel="manifest" href="/static/browse/0.3.4/images/icons/site.webmanifest"> <link rel="mask-icon" href="/static/browse/0.3.4/images/icons/safari-pinned-tab.svg" color="#5bbad5"> <meta name="msapplication-TileColor" content="#da532c"> <meta name="theme-color" content="#ffffff"> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/arXiv.css?v=20241206" /> <link rel="stylesheet" type="text/css" media="print" href="/static/browse/0.3.4/css/arXiv-print.css?v=20200611" /> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/browse_search.css" /> <script language="javascript" src="/static/browse/0.3.4/js/accordion.js" /></script> <link rel="canonical" href="https://arxiv.org/abs/2409.01909"/> <meta name="description" content="Abstract page for arXiv paper 2409.01909: LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models"><meta property="og:type" content="website" /> <meta property="og:site_name" content="arXiv.org" /> <meta property="og:title" content="LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models" /> <meta property="og:url" content="https://arxiv.org/abs/2409.01909v2" /> <meta property="og:image" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:secure_url" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:width" content="1200" /> <meta property="og:image:height" content="700" /> <meta property="og:image:alt" content="arXiv logo"/> <meta property="og:description" content="Logs play a critical role in providing essential information for system monitoring and troubleshooting. Recently, with the success of pre-trained language models (PLMs) and large language models (LLMs) in natural language processing (NLP), smaller PLMs (such as BERT) and LLMs (like GPT-4) have become the current mainstream approaches for log analysis. Despite the remarkable capabilities of LLMs, their higher cost and inefficient inference present significant challenges in leveraging the full potential of LLMs to analyze logs. In contrast, smaller PLMs can be fine-tuned for specific tasks even with limited computational resources, making them more practical. However, these smaller PLMs face challenges in understanding logs comprehensively due to their limited expert knowledge. To address the lack of expert knowledge and enhance log understanding for smaller PLMs, this paper introduces a novel and practical knowledge enhancement framework, called LUK, which acquires expert knowledge from LLMs automatically and then enhances the smaller PLM for log analysis with these expert knowledge. LUK can take full advantage of both types of models. Specifically, we design a multi-expert collaboration framework based on LLMs with different roles to acquire expert knowledge. In addition, we propose two novel pre-training tasks to enhance the log pre-training with expert knowledge. LUK achieves state-of-the-art results on different log analysis tasks and extensive experiments demonstrate expert knowledge from LLMs can be utilized more effectively to understand logs. Our source code and detailed experimental data are available at https://github.com/LeaperOvO/LUK."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="LUK: Empowering Log Understanding with Expert Knowledge from Large..."/> <meta name="twitter:description" content="Logs play a critical role in providing essential information for system monitoring and troubleshooting. Recently, with the success of pre-trained language models (PLMs) and large language models..."/> <meta name="twitter:image" content="https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"/> <meta name="twitter:image:alt" content="arXiv logo"/> <link rel="stylesheet" media="screen" type="text/css" href="/static/browse/0.3.4/css/tooltip.css"/><link rel="stylesheet" media="screen" type="text/css" href="https://static.arxiv.org/js/bibex-dev/bibex.css?20200709"/> <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script> <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/dompurify@2.3.5/dist/purify.min.js"></script> <script src="/static/browse/0.3.4/js/toggle-labs.js?20241022" type="text/javascript"></script> <script src="/static/browse/0.3.4/js/cite.js" type="text/javascript"></script><meta name="citation_title" content="LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models" /><meta name="citation_author" content="Ma, Lipeng" /><meta name="citation_author" content="Yang, Weidong" /><meta name="citation_author" content="Jiang, Sihang" /><meta name="citation_author" content="Fei, Ben" /><meta name="citation_author" content="Zhou, Mingjie" /><meta name="citation_author" content="Li, Shuhao" /><meta name="citation_author" content="Zhao, Mingyu" /><meta name="citation_author" content="Xu, Bo" /><meta name="citation_author" content="Xiao, Yanghua" /><meta name="citation_date" content="2024/09/03" /><meta name="citation_online_date" content="2025/01/31" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2409.01909" /><meta name="citation_arxiv_id" content="2409.01909" /><meta name="citation_abstract" content="Logs play a critical role in providing essential information for system monitoring and troubleshooting. Recently, with the success of pre-trained language models (PLMs) and large language models (LLMs) in natural language processing (NLP), smaller PLMs (such as BERT) and LLMs (like GPT-4) have become the current mainstream approaches for log analysis. Despite the remarkable capabilities of LLMs, their higher cost and inefficient inference present significant challenges in leveraging the full potential of LLMs to analyze logs. In contrast, smaller PLMs can be fine-tuned for specific tasks even with limited computational resources, making them more practical. However, these smaller PLMs face challenges in understanding logs comprehensively due to their limited expert knowledge. To address the lack of expert knowledge and enhance log understanding for smaller PLMs, this paper introduces a novel and practical knowledge enhancement framework, called LUK, which acquires expert knowledge from LLMs automatically and then enhances the smaller PLM for log analysis with these expert knowledge. LUK can take full advantage of both types of models. Specifically, we design a multi-expert collaboration framework based on LLMs with different roles to acquire expert knowledge. In addition, we propose two novel pre-training tasks to enhance the log pre-training with expert knowledge. LUK achieves state-of-the-art results on different log analysis tasks and extensive experiments demonstrate expert knowledge from LLMs can be utilized more effectively to understand logs. Our source code and detailed experimental data are available at https://github.com/LeaperOvO/LUK." /> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a>  <div class="columns is-vcentered is-hidden-mobile" id="cu-identity"> <div class="column" id="cu-logo"> <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a> </div><div class="column" id="support-ack"> <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span> <a href="https://info.arxiv.org/about/donate.html" class="btn-header-donate">Donate</a> </div> </div> <div id="header" class="is-hidden-mobile"> <a aria-hidden="true" tabindex="-1" href="/IgnoreMe"></a> <div class="header-breadcrumbs is-hidden-mobile"> <a 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href="https://arxiv.org/search/cs?searchtype=author&query=Ma,+L" rel="nofollow">Lipeng Ma</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+W" rel="nofollow">Weidong Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jiang,+S" rel="nofollow">Sihang Jiang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fei,+B" rel="nofollow">Ben Fei</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+M" rel="nofollow">Mingjie Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+S" rel="nofollow">Shuhao Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+M" rel="nofollow">Mingyu Zhao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+B" rel="nofollow">Bo Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xiao,+Y" rel="nofollow">Yanghua Xiao</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models, by Lipeng Ma and 8 other authors</div> <a class="mobile-submission-download" href="/pdf/2409.01909">View PDF</a> <a class="mobile-submission-download" href="https://arxiv.org/html/2409.01909v2">HTML (experimental)</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>Logs play a critical role in providing essential information for system monitoring and troubleshooting. Recently, with the success of pre-trained language models (PLMs) and large language models (LLMs) in natural language processing (NLP), smaller PLMs (such as BERT) and LLMs (like GPT-4) have become the current mainstream approaches for log analysis. Despite the remarkable capabilities of LLMs, their higher cost and inefficient inference present significant challenges in leveraging the full potential of LLMs to analyze logs. In contrast, smaller PLMs can be fine-tuned for specific tasks even with limited computational resources, making them more practical. However, these smaller PLMs face challenges in understanding logs comprehensively due to their limited expert knowledge. To address the lack of expert knowledge and enhance log understanding for smaller PLMs, this paper introduces a novel and practical knowledge enhancement framework, called LUK, which acquires expert knowledge from LLMs automatically and then enhances the smaller PLM for log analysis with these expert knowledge. LUK can take full advantage of both types of models. Specifically, we design a multi-expert collaboration framework based on LLMs with different roles to acquire expert knowledge. In addition, we propose two novel pre-training tasks to enhance the log pre-training with expert knowledge. LUK achieves state-of-the-art results on different log analysis tasks and extensive experiments demonstrate expert knowledge from LLMs can be utilized more effectively to understand logs. Our source code and detailed experimental data are available at <a href="https://github.com/LeaperOvO/LUK" rel="external noopener nofollow" class="link-external link-https">this https URL</a>. </blockquote>  <div class="metatable"> <table summary="Additional metadata"> <tr> <td class="tablecell label">Comments:</td> <td class="tablecell comments mathjax">Under review</td> </tr> <tr> <td class="tablecell label">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Software Engineering (cs.SE)</span>; Artificial Intelligence (cs.AI)</td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2409.01909">arXiv:2409.01909</a> [cs.SE]</span></td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/2409.01909v2">arXiv:2409.01909v2</a> [cs.SE]</span> for this version) </td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxivdoi"> <a href="https://doi.org/10.48550/arXiv.2409.01909" id="arxiv-doi-link">https://doi.org/10.48550/arXiv.2409.01909</a><div class="button-and-tooltip"> <button class="more-info" aria-describedby="more-info-desc-1"> <svg height="15" role="presentation" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 110c23.196 0 42 18.804 42 42s-18.804 42-42 42-42-18.804-42-42 18.804-42 42-42zm56 254c0 6.627-5.373 12-12 12h-88c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h12v-64h-12c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h64c6.627 0 12 5.373 12 12v100h12c6.627 0 12 5.373 12 12v24z" class=""></path></svg> <span class="visually-hidden">Focus to learn more</span> </button>  <div role="tooltip" id="more-info-desc-1"> <span class="left-corner"></span> arXiv-issued DOI via DataCite</div> </div> </td> </tr></table> </div> </div> </div> <div class="submission-history"> <h2>Submission history</h2> From: Lipeng Ma [<a href="/show-email/5f5d5d2a/2409.01909" rel="nofollow">view email</a>] <br/> <strong><a href="/abs/2409.01909v1" rel="nofollow">[v1]</a></strong> Tue, 3 Sep 2024 13:58:34 UTC (1,435 KB)<br/> <strong>[v2]</strong> Fri, 31 Jan 2025 05:51:52 UTC (8,604 KB)<br/> </div> </div>  <div class="extra-services"> <div class="full-text"> <a name="other"></a> <span class="descriptor">Full-text links:</span> <h2>Access Paper:</h2> <ul> <div id="download-button-info" hidden> View a PDF of the paper titled LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models, by Lipeng Ma and 8 other authors</div><li><a href="/pdf/2409.01909" aria-describedby="download-button-info" accesskey="f" class="abs-button download-pdf">View PDF</a></li><li><a href="https://arxiv.org/html/2409.01909v2" class="abs-button" id="latexml-download-link">HTML (experimental)</a></li><li><a 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[2409.01909] LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models