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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Holistic Evaluation of Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2211.09110/</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/2211.09110/feed.xml" rel="self" type="application/rss+xml" /><item><title>Holistic Evaluation of Language Models — mentioned by ninjin</title><link>https://news.ycombinator.com/item?id=35337906</link><guid isPermaLink="false">urn:hackerpapers:mention:2211.09110:35337906</guid><pubDate>Tue, 28 Mar 2023 09:27:45 GMT</pubDate><description>&lt;p&gt;I am actually not quite sure what would be best to recommend, as the pandemic has seen me lag behind the zeitgeist somewhat. A recent favourite would be the Toolformer paper [1], that I intend to read in detail later today. If an LLM would be able to use external tools efficiently, it could be rather powerful and perhaps allow us to scale down the parameter sizes, which somewhat fascinates me. [1]: https://arxiv.org/abs/2302.04761 Other research questions, but without concrete papers to reference to that current keeps me up at night: 1.) to which degree can we train substantially smaller LLMs…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2211.09110/"&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>
