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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Train-before-Test Harmonizes Language Model Rankings · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2507.05195/</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/2507.05195/feed.xml" rel="self" type="application/rss+xml" /><item><title>Train-before-Test Harmonizes Language Model Rankings — mentioned by yorwba</title><link>https://news.ycombinator.com/item?id=47994060</link><guid isPermaLink="false">urn:hackerpapers:mention:2507.05195:47994060</guid><pubDate>Sun, 03 May 2026 06:42:24 GMT</pubDate><description>&lt;p&gt;There are objective ways to compare models. They involve repeated sampling and statistical analysis to determine whether the results are likely to hold up in the future or whether they&amp;#x27;re just a fluke. If you fine-tune each model to achieve its full potential on the task you expect to be giving it, the rankings produced by different benchmarks even agree to a high degree: https://arxiv.org/abs/2507.05195 The author didn&amp;#x27;t do any of that. They ran each model once on each of 13 (so far) problems and then they chose to highlight the results for the 12th problem. That&amp;#x27;s not even p-hacking…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2507.05195/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
