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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Generalization through Memorization: Nearest Neighbor Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1911.00172/</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/1911.00172/feed.xml" rel="self" type="application/rss+xml" /><item><title>Generalization through Memorization: Nearest Neighbor Language Models — mentioned by jpfed</title><link>https://news.ycombinator.com/item?id=37296562</link><guid isPermaLink="false">urn:hackerpapers:mention:1911.00172:37296562</guid><pubDate>Mon, 28 Aug 2023 16:24:09 GMT</pubDate><description>&lt;p&gt;The most promising work along these lines centers around augmenting LLMs with an external data store (&amp;quot;retrieval-augmented LLM&amp;quot;s). I think this started with Facebook&amp;#x27;s KNN-LLM ( https://arxiv.org/pdf/1911.00172.pdf ). Legal conflict may force the industry to move towards vector DBs as the predominant method by which facts are &amp;quot;stored&amp;quot; rather than model parameters ( https://arxiv.org/pdf/2308.04430.pdf ) , with the happy side effect of update-ability over time.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1911.00172/"&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>
