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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Detecting Corpus-Level Knowledge Inconsistencies in Wikipedia with Large Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2509.23233/</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/2509.23233/feed.xml" rel="self" type="application/rss+xml" /><item><title>Detecting Corpus-Level Knowledge Inconsistencies in Wikipedia with Large Language Models — mentioned by vintermann</title><link>https://news.ycombinator.com/item?id=46678262</link><guid isPermaLink="false">urn:hackerpapers:mention:2509.23233:46678262</guid><pubDate>Mon, 19 Jan 2026 12:31:29 GMT</pubDate><description>&lt;p&gt;There was a paper recently about using LLMs to find contradictions in Wikipedia, i.e. claims on the same page or between pages which appear to be mutually incompatible. https://arxiv.org/abs/2509.23233 I wonder if something more came out of that. Either way, I think that generation of article text is the least useful and interesting way to use AI on Wikipedia. It&amp;#x27;s much better to do things like this paper did.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2509.23233/"&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>
