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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Demystifying Verbatim Memorization in Large Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2407.17817/</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/2407.17817/feed.xml" rel="self" type="application/rss+xml" /><item><title>Demystifying Verbatim Memorization in Large Language Models — mentioned by ks2048</title><link>https://news.ycombinator.com/item?id=42538257</link><guid isPermaLink="false">urn:hackerpapers:mention:2407.17817:42538257</guid><pubDate>Sun, 29 Dec 2024 07:09:14 GMT</pubDate><description>&lt;p&gt;This is interesting. I&amp;#x27;m curious about how much (and what) these LLMs memorize verbatim. Does anyone know any more thorough papers on this topic? For example, this could be tested on every verse in bible and lots of other text that is certainly in the training data: books in project gutenberg, wikipedia articles, etc. Edit: this (and its references) looks like a good place to start: https://arxiv.org/abs/2407.17817v1&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2407.17817/"&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>
