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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2406.11695/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2406.11695/feed.xml" rel="self" type="application/rss+xml" /><item><title>Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs — mentioned by abhgh</title><link>https://news.ycombinator.com/item?id=46288081</link><guid isPermaLink="false">urn:hackerpapers:mention:2406.11695:46288081</guid><pubDate>Tue, 16 Dec 2025 13:12:05 GMT</pubDate><description>&lt;p&gt;Thank you. Your comment about LLMs to semantically parse diverse data, as a first step, makes sense. In fact come to think of it, in the area of prompt optimization too - such as MIPROv2 [1] - the LLM is used to create initial prompt guesses based on its understanding of data. And I agree that UMAP still works well out of the box and has been pretty much like this since its introduction. [1] Section C.1 in the Appendix here https://arxiv.org/pdf/2406.11695&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2406.11695/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
