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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Model Consistency as a Cheap yet Predictive Proxy for LLM Elo Scores · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2509.23510/</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/2509.23510/feed.xml" rel="self" type="application/rss+xml" /><item><title>Model Consistency as a Cheap yet Predictive Proxy for LLM Elo Scores — mentioned by nestorD</title><link>https://news.ycombinator.com/item?id=46547250</link><guid isPermaLink="false">urn:hackerpapers:mention:2509.23510:46547250</guid><pubDate>Thu, 08 Jan 2026 22:14:57 GMT</pubDate><description>&lt;p&gt;On alternative ways to measure LLM intelligence, we had good success with this: https://arxiv.org/abs/2509.23510 In short: start with a dataset of question and answer pairs, where each question has been answered by two different LLMs. Ask the model you want to evaluate to choose the better answer for each pair. Then measure how consistently it selects winners. Does it reliably favor some models over the questions, or does it behave close to randomly? This consistency is a strong proxy for the model’s intelligence. It is not subject to dataset leaks, lets you measure intelligence in many…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2509.23510/"&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>
