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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Generate rather than Retrieve: Large Language Models are Strong Context Generators · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2209.10063/</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/2209.10063/feed.xml" rel="self" type="application/rss+xml" /><item><title>Generate rather than Retrieve: Large Language Models are Strong Context Generators — mentioned by bestcoder69</title><link>https://news.ycombinator.com/item?id=35050886</link><guid isPermaLink="false">urn:hackerpapers:mention:2209.10063:35050886</guid><pubDate>Tue, 07 Mar 2023 02:42:52 GMT</pubDate><description>&lt;p&gt;See also GenRead[0]: &amp;gt; A common approach for knowledge-intensive tasks is to employ a retrieve-then-read pipeline that first retrieves a handful of relevant contextual documents from an external corpus such as Wikipedia and then predicts an answer conditioned on the retrieved documents. In this paper, we present a novel perspective for solving knowledge-intensive tasks by replacing document retrievers with large language model generators. i.e. generating some bullshit citation will improve the accuracy of the completion -- even outperforming actual retrieval! (they find, at least) Also see…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2209.10063/"&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>
