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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Revisiting Multi-Modal LLM Evaluation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2408.05334/</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/2408.05334/feed.xml" rel="self" type="application/rss+xml" /><item><title>Revisiting Multi-Modal LLM Evaluation — mentioned by chriskanan</title><link>https://news.ycombinator.com/item?id=41657089</link><guid isPermaLink="false">urn:hackerpapers:mention:2408.05334:41657089</guid><pubDate>Thu, 26 Sep 2024 11:26:03 GMT</pubDate><description>&lt;p&gt;The assessments of visual capability really need to be more robust. They are still using datasets like VQAv2, which while providing some insight, have many issues. There are many newer datasets that serve as much more robust tests and that are less prone to being affected by linguistic bias. I&amp;#x27;d like to see more head-to-head comparisons with community created multi-modal LLMs as done in these papers: https://arxiv.org/abs/2408.05334 https://arxiv.org/abs/2408.03326 I look forward to reading the technical report, once its available. I couldn&amp;#x27;t find a link to one, yet.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2408.05334/"&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>
