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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>Yash Jain</author_name><author_url>https://www.microsoft.com/en-us/research/people/yasjain/</author_url><title>Local Prompt Optimization - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="vmCi1klxKS"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/local-prompt-optimization/"&gt;Local Prompt Optimization&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/local-prompt-optimization/embed/#?secret=vmCi1klxKS" width="600" height="338" title="&#x201C;Local Prompt Optimization&#x201D; &#x2014; Microsoft Research" data-secret="vmCi1klxKS" 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 recent years, the use of prompts to guide the output of Large Language Models have increased dramatically. However, even the best of experts struggle to choose the correct words to stitch up a prompt for the desired task. To solve this, LLM driven prompt optimization emerged as an important problem. Existing prompt optimization methods [&hellip;]</description></oembed>
