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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A Survey on Contextual Embeddings · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2003.07278/</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/2003.07278/feed.xml" rel="self" type="application/rss+xml" /><item><title>A Survey on Contextual Embeddings — mentioned by WhitneyLand</title><link>https://news.ycombinator.com/item?id=38511464</link><guid isPermaLink="false">urn:hackerpapers:mention:2003.07278:38511464</guid><pubDate>Sun, 03 Dec 2023 22:36:04 GMT</pubDate><description>&lt;p&gt;As a prerequisite to the attention paper? One to check out is: A Survey on Contextual Embeddings https://arxiv.org/abs/2003.07278 Embeddings are sort of what all this stuff is built on so it should help demystify the newer papers (it’s actually newer than the attention paper but a better overview than starting with the older word2vec paper). Then after the attention paper an important one is: Language Models are Few-Shot Learners https://arxiv.org/abs/2005.14165 I’m intentionally trying to not give a big list because they’re so time-consuming. I’m sure you’ll quickly branch out based on your…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2003.07278/"&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>
