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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Causality for Large Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2410.15319/</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/2410.15319/feed.xml" rel="self" type="application/rss+xml" /><item><title>Causality for Large Language Models — mentioned by esafak</title><link>https://news.ycombinator.com/item?id=45441430</link><guid isPermaLink="false">urn:hackerpapers:mention:2410.15319:45441430</guid><pubDate>Wed, 01 Oct 2025 18:34:55 GMT</pubDate><description>&lt;p&gt;There are papers on that, such as https://arxiv.org/abs/2410.15319. Time series modeling will not bring about an understanding of causality except in a weak sense https://en.wikipedia.org/wiki/Granger_causality. To truly connect a cause and effect you need a graphical model. And automated causal discovery, the hardest part of which is proposing the nodes of the graph, is a nascent field.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2410.15319/"&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>
