[2411.07763] Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows

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We introduce Spider 2.0, an evaluation framework comprising 632 real-world text-to-SQL workflow problems derived from enterprise-level database use cases. The databases in Spider 2.0 are sourced from real data applications, often containing over 1,000 columns and stored in local or cloud database systems such as BigQuery and Snowflake. We show that solving problems in Spider 2.0 frequently requires understanding and searching through database metadata, dialect documentation, and even project-level codebases. This challenge calls for models to interact with complex SQL workflow environments, process extremely long contexts, perform intricate reasoning, and generate multiple SQL queries with diverse operations, often exceeding 100 lines, which goes far beyond traditional text-to-SQL challenges. Our evaluations indicate that based on o1-preview, our code agent framework successfully solves only 17.0% of the tasks, compared with 91.2% on Spider 1.0 and 73.0% on BIRD. Our results on Spider 2.0 show that while language models have demonstrated remarkable performance in code generation -- especially in prior text-to-SQL benchmarks -- they require significant improvement in order to achieve adequate performance for real-world enterprise usage. Progress on Spider 2.0 represents crucial steps towards developing intelligent, autonomous, code agents for real-world enterprise settings. Our code, baseline models, and data are available at https://spider2-sql.github.io."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="Spider 2.0: Evaluating Language Models on Real-World Enterprise..."/> <meta name="twitter:description" content="Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data..."/> <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="Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows" /><meta name="citation_author" content="Lei, Fangyu" /><meta name="citation_author" content="Chen, Jixuan" /><meta name="citation_author" content="Ye, Yuxiao" /><meta name="citation_author" content="Cao, Ruisheng" /><meta name="citation_author" content="Shin, Dongchan" /><meta name="citation_author" content="Su, Hongjin" /><meta name="citation_author" content="Suo, Zhaoqing" /><meta name="citation_author" content="Gao, Hongcheng" /><meta name="citation_author" content="Hu, Wenjing" /><meta name="citation_author" content="Yin, Pengcheng" /><meta name="citation_author" content="Zhong, Victor" /><meta name="citation_author" content="Xiong, Caiming" /><meta name="citation_author" content="Sun, Ruoxi" /><meta name="citation_author" content="Liu, Qian" /><meta name="citation_author" content="Wang, Sida" /><meta name="citation_author" content="Yu, Tao" /><meta name="citation_date" content="2024/11/12" /><meta name="citation_online_date" content="2024/11/12" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2411.07763" /><meta name="citation_arxiv_id" content="2411.07763" /><meta name="citation_abstract" content="Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data transformation to analytics. We introduce Spider 2.0, an evaluation framework comprising 632 real-world text-to-SQL workflow problems derived from enterprise-level database use cases. The databases in Spider 2.0 are sourced from real data applications, often containing over 1,000 columns and stored in local or cloud database systems such as BigQuery and Snowflake. We show that solving problems in Spider 2.0 frequently requires understanding and searching through database metadata, dialect documentation, and even project-level codebases. This challenge calls for models to interact with complex SQL workflow environments, process extremely long contexts, perform intricate reasoning, and generate multiple SQL queries with diverse operations, often exceeding 100 lines, which goes far beyond traditional text-to-SQL challenges. Our evaluations indicate that based on o1-preview, our code agent framework successfully solves only 17.0% of the tasks, compared with 91.2% on Spider 1.0 and 73.0% on BIRD. Our results on Spider 2.0 show that while language models have demonstrated remarkable performance in code generation -- especially in prior text-to-SQL benchmarks -- they require significant improvement in order to achieve adequate performance for real-world enterprise usage. Progress on Spider 2.0 represents crucial steps towards developing intelligent, autonomous, code agents for real-world enterprise settings. Our code, baseline models, and data are available at https://spider2-sql.github.io." /> </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" href="{url_path('ignore_me')}"></a> <div class="header-breadcrumbs is-hidden-mobile"> <a href="/"><img 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<a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+J" rel="nofollow">Jixuan Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ye,+Y" rel="nofollow">Yuxiao Ye</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cao,+R" rel="nofollow">Ruisheng Cao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shin,+D" rel="nofollow">Dongchan Shin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Su,+H" rel="nofollow">Hongjin Su</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Suo,+Z" rel="nofollow">Zhaoqing Suo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gao,+H" rel="nofollow">Hongcheng Gao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hu,+W" rel="nofollow">Wenjing Hu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yin,+P" rel="nofollow">Pengcheng Yin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhong,+V" rel="nofollow">Victor Zhong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xiong,+C" rel="nofollow">Caiming Xiong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+R" rel="nofollow">Ruoxi Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+Q" rel="nofollow">Qian Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+S" rel="nofollow">Sida Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+T" rel="nofollow">Tao Yu</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows, by Fangyu Lei and 15 other authors</div> <a class="mobile-submission-download" href="/pdf/2411.07763">View PDF</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data transformation to analytics. We introduce Spider 2.0, an evaluation framework comprising 632 real-world text-to-SQL workflow problems derived from enterprise-level database use cases. The databases in Spider 2.0 are sourced from real data applications, often containing over 1,000 columns and stored in local or cloud database systems such as BigQuery and Snowflake. We show that solving problems in Spider 2.0 frequently requires understanding and searching through database metadata, dialect documentation, and even project-level codebases. This challenge calls for models to interact with complex SQL workflow environments, process extremely long contexts, perform intricate reasoning, and generate multiple SQL queries with diverse operations, often exceeding 100 lines, which goes far beyond traditional text-to-SQL challenges. Our evaluations indicate that based on o1-preview, our code agent framework successfully solves only 17.0% of the tasks, compared with 91.2% on Spider 1.0 and 73.0% on BIRD. Our results on Spider 2.0 show that while language models have demonstrated remarkable performance in code generation -- especially in prior text-to-SQL benchmarks -- they require significant improvement in order to achieve adequate performance for real-world enterprise usage. Progress on Spider 2.0 represents crucial steps towards developing intelligent, autonomous, code agents for real-world enterprise settings. Our code, baseline models, and data are available at <a href="https://spider2-sql.github.io" 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">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Computation and Language (cs.CL)</span>; Artificial Intelligence (cs.AI); Databases (cs.DB)</td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2411.07763">arXiv:2411.07763</a> [cs.CL]</span></td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/2411.07763v1">arXiv:2411.07763v1</a> [cs.CL]</span> for this version) </td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxivdoi"> <a href="https://doi.org/10.48550/arXiv.2411.07763" id="arxiv-doi-link">https://doi.org/10.48550/arXiv.2411.07763</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 (pending registration)</div> </div> </td> </tr></table> </div> </div> </div> <div class="submission-history"> <h2>Submission history</h2> From: Fangyu Lei [<a href="/show-email/4720e993/2411.07763" rel="nofollow">view email</a>] <br/> <strong>[v1]</strong> Tue, 12 Nov 2024 12:52:17 UTC (5,260 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 Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows, by Fangyu Lei and 15 other authors</div><li><a href="/pdf/2411.07763" aria-describedby="download-button-info" accesskey="f" class="abs-button download-pdf">View PDF</a></li><li><a href="/src/2411.07763" class="abs-button download-eprint">TeX Source</a></li><li><a href="/format/2411.07763" class="abs-button download-format">Other Formats</a></li></ul> <div class="abs-license"><a href="http://arxiv.org/licenses/nonexclusive-distrib/1.0/" title="Rights to this article">view 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[2411.07763] Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows