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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Yuqing Yang</author_name><author_url>https://www.microsoft.com/en-us/research/people/yuqyang/</author_url><title>Benchmarking Data Science Agents - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="6Kth3qs9WF"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/benchmarking-data-science-agents/"&gt;Benchmarking Data Science Agents&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/benchmarking-data-science-agents/embed/#?secret=6Kth3qs9WF" width="600" height="338" title="&#x201C;Benchmarking Data Science Agents&#x201D; &#x2014; Microsoft Research" data-secret="6Kth3qs9WF" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>In the era of data-driven decision-making, the complexity of data analysis necessitates advanced expertise and tools of data science, presenting significant challenges even for specialists. Large Language Models (LLMs) have emerged as promising aids as data science agents, assisting humans in data analysis and processing. Yet their practical efficacy remains constrained by the varied demands [&hellip;]</description></oembed>
