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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SPECTER: Document-level Representation Learning using Citation-informed Transformers · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2004.07180/</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/2004.07180/feed.xml" rel="self" type="application/rss+xml" /><item><title>SPECTER: Document-level Representation Learning using Citation-informed Transformers — mentioned by smeeth</title><link>https://news.ycombinator.com/item?id=33614608</link><guid isPermaLink="false">urn:hackerpapers:mention:2004.07180:33614608</guid><pubDate>Tue, 15 Nov 2022 20:31:59 GMT</pubDate><description>&lt;p&gt;I use scientific language models professionally. I skimmed the paper and was immediately disappointed. - They benchmarked against general models like GPT-3 but not well-established specific models that have been trained for specific tasks like SPECTER[0] or SciBert[1]. Specter outperformed GPT-3 on tasks like citation prediction two years ago. Nobody seriously uses general LLMs on science tasks, so nobody who actually wants to use this cares about your benchmarks. I want to see task-specific models compared to your general model, otherwise whats going to happen is I either need to run my own…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2004.07180/"&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>
