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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Explaining Length Bias in LLM-Based Preference Evaluations · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2407.01085/</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/2407.01085/feed.xml" rel="self" type="application/rss+xml" /><item><title>Explaining Length Bias in LLM-Based Preference Evaluations — mentioned by mh-</title><link>https://news.ycombinator.com/item?id=44832641</link><guid isPermaLink="false">urn:hackerpapers:mention:2407.01085:44832641</guid><pubDate>Fri, 08 Aug 2025 02:02:42 GMT</pubDate><description>&lt;p&gt;Thanks for that pointer. The abstract of this paper seems interesting: https://arxiv.org/html/2407.01085v3 &amp;gt; use of [LLMs] as judges [..] reveals a notable bias towards longer responses, undermining the reliability of such evaluations. To better understand such bias, we propose to decompose the preference evaluation metric, specifically the win rate, into two key components: desirability and information mass [..] (If you&amp;#x27;re interested, give it a click. I tried to pare this down to avoid quoting a wall of text.)&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2407.01085/"&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>
