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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Training Verifiers to Solve Math Word Problems · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2110.14168/</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/2110.14168/feed.xml" rel="self" type="application/rss+xml" /><item><title>Training Verifiers to Solve Math Word Problems — mentioned by dekhn</title><link>https://news.ycombinator.com/item?id=37476213</link><guid isPermaLink="false">urn:hackerpapers:mention:2110.14168:37476213</guid><pubDate>Tue, 12 Sep 2023 02:01:33 GMT</pubDate><description>&lt;p&gt;During the big GPT-4 news cycle I think a bunch of folks posted claims that were outrageously good- &amp;quot;language model passes medical exams better than humans&amp;quot;, etc. When I looked into them, in nearly all cases, the claims were boosted far beyond the reality. And the reality seemed much more consistent with a fairly banal interpretation: LLMs produce realistic looking text but have no real ability to distinguish truth from fabrication (which is a step beyond bullshit!). The one example that still interests me is math problem solving. Can next-token predictors really solve generalized math…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2110.14168/"&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>
