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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>General-Purpose Question-Answering with Macaw · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2109.02593/</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/2109.02593/feed.xml" rel="self" type="application/rss+xml" /><item><title>General-Purpose Question-Answering with Macaw — mentioned by danielmorozoff</title><link>https://news.ycombinator.com/item?id=30067277</link><guid isPermaLink="false">urn:hackerpapers:mention:2109.02593:30067277</guid><pubDate>Tue, 25 Jan 2022 02:49:30 GMT</pubDate><description>&lt;p&gt;The MACAW model is quite impressive -- it significantly outperforms GTP3 on Q&amp;amp;A/reasoning tasks (~10%) while requiring x10 less parameters. It is based on T5 , which is a well known model from Google. The novel innovation here is the training paradigm. Also did I mention it&amp;#x27;s OSS: https://macaw.apps.allenai.org/ Here is the paper: https://arxiv.org/abs/2109.02593&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2109.02593/"&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>
