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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2212.14024/</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/2212.14024/feed.xml" rel="self" type="application/rss+xml" /><item><title>Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP — mentioned by svcrunch</title><link>https://news.ycombinator.com/item?id=37435147</link><guid isPermaLink="false">urn:hackerpapers:mention:2212.14024:37435147</guid><pubDate>Fri, 08 Sep 2023 15:45:31 GMT</pubDate><description>&lt;p&gt;I believe that retrieval-augmented generation is the right path to generative AI within organizations, at least for the next few years. Trying to directly fine-tune an LLM on your data also runs into issues with enforcing access permissions. However, instead of simply being a post-processing step at the end of an IR pipeline, LLMs will eventually sandwhich the IR system, along the lines of the [Demonstrate, Search, Predict framework](https://arxiv.org/abs/2212.14024) by Khattab et al.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2212.14024/"&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>
