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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Truth is Universal: Robust Detection of Lies in LLMs · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2407.12831/</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/2407.12831/feed.xml" rel="self" type="application/rss+xml" /><item><title>Truth is Universal: Robust Detection of Lies in LLMs — mentioned by arw0n</title><link>https://news.ycombinator.com/item?id=46242230</link><guid isPermaLink="false">urn:hackerpapers:mention:2407.12831:46242230</guid><pubDate>Fri, 12 Dec 2025 09:03:09 GMT</pubDate><description>&lt;p&gt;I think the better word is confabulation; fabricating plausible but false narratives based on wrong memory. Fundamentally, these models try to produce plausible text. With language models getting large, they start creating internal world models, and some research shows they actually have truth dimensions. [0] I&amp;#x27;m not an expert on the topic, but to me it sounds plausible that a good part of the problem of confabulation comes down to misaligned incentives. These models are trained hard to be a &amp;#x27;helpful assistant&amp;#x27;, and this might conflict with telling the truth. Being free of hallucinations is a…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2407.12831/"&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>
