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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Measuring Non-Adversarial Reproduction of Training Data in Large Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2411.10242/</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/2411.10242/feed.xml" rel="self" type="application/rss+xml" /><item><title>Measuring Non-Adversarial Reproduction of Training Data in Large Language Models — mentioned by aaronharnly</title><link>https://news.ycombinator.com/item?id=49647690</link><guid isPermaLink="false">urn:hackerpapers:mention:2411.10242:49647690</guid><pubDate>Thu, 10 Sep 2026 17:49:53 GMT</pubDate><description>&lt;p&gt;* with train on my data turned ON, yes. Though OFF would of course be even more notable! Thank you – the non-adversarial reproduction paper ( https://arxiv.org/abs/2411.10242 ) nails it – from chat, to training corpus, to subsequent model. Though in my hasty read, it is not entirely clear whether the snippets it finds are nonces, i.e. present exactly once in the internet.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2411.10242/"&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>
