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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Personas as a Way to Model Truthfulness in Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2310.18168/</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/2310.18168/feed.xml" rel="self" type="application/rss+xml" /><item><title>Personas as a Way to Model Truthfulness in Language Models — mentioned by astrange</title><link>https://news.ycombinator.com/item?id=40478293</link><guid isPermaLink="false">urn:hackerpapers:mention:2310.18168:40478293</guid><pubDate>Sat, 25 May 2024 22:26:18 GMT</pubDate><description>&lt;p&gt;Some things like: https://arxiv.org/abs/2310.18168 https://arxiv.org/abs/2310.06824 There are various reasons an LLM might have incorrect &amp;quot;beliefs&amp;quot; - the input text was false, training doesn&amp;#x27;t try to preserve true beliefs, quantization certainly doesn&amp;#x27;t. So it can&amp;#x27;t be perfectly addressed, but some things leading to it seem like they can be found. &amp;gt; https://arxiv.org/abs/2401.11817 This seems like it&amp;#x27;s true since LLMs are a finite size, but in Google&amp;#x27;s case it has a &amp;quot;truth oracle&amp;quot; (the websites it&amp;#x27;s quoting)… the problem is it&amp;#x27;s a bad oracle.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2310.18168/"&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>
