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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>ConfidenceBench: Evaluating Confidence Calibration in Large Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2607.20526/</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/2607.20526/feed.xml" rel="self" type="application/rss+xml" /><item><title>ConfidenceBench: Evaluating Confidence Calibration in Large Language Models — mentioned by epihelix</title><link>https://news.ycombinator.com/item?id=50191191</link><guid isPermaLink="false">urn:hackerpapers:mention:2607.20526:50191191</guid><pubDate>Thu, 22 Oct 2026 04:30:20 GMT</pubDate><description>&lt;p&gt;You mean like this recent preprint? https://arxiv.org/html/2607.20526v1 From that paper, model accuracy is not perfect, but it&amp;#x27;s overall pretty favourable. There are some encouraging findings in there, too, including that accuracy of model confidence scales with reasoning effort.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2607.20526/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
