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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Approximating CKY with Transformers · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2305.02386/</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/2305.02386/feed.xml" rel="self" type="application/rss+xml" /><item><title>Approximating CKY with Transformers — mentioned by thomasahle</title><link>https://news.ycombinator.com/item?id=36465772</link><guid isPermaLink="false">urn:hackerpapers:mention:2305.02386:36465772</guid><pubDate>Sun, 25 Jun 2023 07:24:42 GMT</pubDate><description>&lt;p&gt;&amp;gt; LLM&amp;#x27;s don&amp;#x27;t actually understand anything (as greater concepts), but rather operate as complex probability machines? What does it mean to &amp;quot;operate as a probability machine&amp;quot;? And what does it mean to understand anything? One recent example of understanding is that llms/transformers learn to parse context free grammars via dynamic programming (https://arxiv.org/abs/2305.02386). Basically they&amp;#x27;ve understood what&amp;#x27;s going on well enough to mold their neurons I to the optimal algorthm for parsing this kind of text. I think they understand lots of things like this. Of course there&amp;#x27;s other things…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2305.02386/"&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>
