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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>MADLAD-400: A Multilingual And Document-Level Large Audited Dataset · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2309.04662/</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/2309.04662/feed.xml" rel="self" type="application/rss+xml" /><item><title>MADLAD-400: A Multilingual And Document-Level Large Audited Dataset — mentioned by ugh123</title><link>https://news.ycombinator.com/item?id=37493012</link><guid isPermaLink="false">urn:hackerpapers:mention:2309.04662:37493012</guid><pubDate>Wed, 13 Sep 2023 06:40:53 GMT</pubDate><description>&lt;p&gt;From the arxiv.org paper: &amp;quot;We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on CommonCrawl, spanning 419 languages. We discuss the limitations revealed by self-auditing MADLAD-400, and the role data auditing had in the dataset creation process. We then train and release a 10.7B-parameter multilingual machine translation model on 250 billion tokens covering over 450 languages using publicly available data, and find that it is competitive with models that are significantly larger, and report the results on different domains. In addition, we train a…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2309.04662/"&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>
