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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>oLMpics -- On what Language Model Pre-training Captures · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1912.13283/</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/1912.13283/feed.xml" rel="self" type="application/rss+xml" /><item><title>oLMpics -- On what Language Model Pre-training Captures — mentioned by rmrfstar</title><link>https://news.ycombinator.com/item?id=23018637</link><guid isPermaLink="false">urn:hackerpapers:mention:1912.13283:23018637</guid><pubDate>Wed, 29 Apr 2020 13:08:54 GMT</pubDate><description>&lt;p&gt;&amp;gt; at some point admit that we want machine learning algorithms to be able to interface with symbolic rules rather than pretending that everything is a data issue That is a long way off. We find ourselves in a situation where we have models that do useful things, but are also horrendously biased. If you want improvement now, the quickest way to get it is recasting benchmarks. Most NLP research groups just hill climb on GLUE or whatever. What gets measured gets managed, and bias is not being systematically measured. Right now language models can&amp;#x27;t even compare ages that fall outside the 20-100…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1912.13283/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
