<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>ColdGANs: Taming Language GANs with Cautious Sampling Strategies · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2006.04643/</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/2006.04643/feed.xml" rel="self" type="application/rss+xml" /><item><title>ColdGANs: Taming Language GANs with Cautious Sampling Strategies — mentioned by ctoth</title><link>https://news.ycombinator.com/item?id=24246738</link><guid isPermaLink="false">urn:hackerpapers:mention:2006.04643:24246738</guid><pubDate>Sat, 22 Aug 2020 19:46:12 GMT</pubDate><description>&lt;p&gt;I keep wanting to write a long explanation of just why this is so... silly? to read? But Gwern has already done the hard work. [0] The only other bit I&amp;#x27;d like to mention is that GPT-3 uses exactly none of the new techniques that have been coming out in the last two years that would have significant impact on text generation. From working methods to apply GANs to text, to far more efficient transformer models that can handle longer sequences. For instance [1] [2] [3] for better direction, or [4] [5] [6] for efficiency. Or perhaps the outside view might help. After seeing GPT-2 last year, did…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2006.04643/"&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>
