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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2508.07111/</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/2508.07111/feed.xml" rel="self" type="application/rss+xml" /><item><title>Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution — mentioned by skupig</title><link>https://news.ycombinator.com/item?id=48401488</link><guid isPermaLink="false">urn:hackerpapers:mention:2508.07111:48401488</guid><pubDate>Thu, 04 Jun 2026 17:05:27 GMT</pubDate><description>&lt;p&gt;There has been plenty of research that shows LLMs encode social biases. It seems pretty obvious even before looking at the research that training on the whole internet will end up encoding widely-held social biases and stereotypes. https://arxiv.org/pdf/2508.07111 https://github.com/angl1n/social-bias-llm-vlm&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2508.07111/"&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>
