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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Censored LLMs as a Natural Testbed for Secret Knowledge Elicitation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2603.05494/</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/2603.05494/feed.xml" rel="self" type="application/rss+xml" /><item><title>Censored LLMs as a Natural Testbed for Secret Knowledge Elicitation — mentioned by throwaway219450</title><link>https://news.ycombinator.com/item?id=49578380</link><guid isPermaLink="false">urn:hackerpapers:mention:2603.05494:49578380</guid><pubDate>Sat, 05 Sep 2026 16:55:42 GMT</pubDate><description>&lt;p&gt;There&amp;#x27;s some cool research that looks at how strongly the weights are aligned through training vs adherence to the system prompt. Like when you know a model is lying through censorship: https://arxiv.org/html/2603.05494v2 Presumably if negative guidance is in the system prompt, there&amp;#x27;s a good chance that the model would happily comply if it wasn&amp;#x27;t there.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2603.05494/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
