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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Arithmetic with Language Models: from Memorization to Computation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2308.01154/</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/2308.01154/feed.xml" rel="self" type="application/rss+xml" /><item><title>Arithmetic with Language Models: from Memorization to Computation — mentioned by red75prime</title><link>https://news.ycombinator.com/item?id=42696385</link><guid isPermaLink="false">urn:hackerpapers:mention:2308.01154:42696385</guid><pubDate>Tue, 14 Jan 2025 12:23:55 GMT</pubDate><description>&lt;p&gt;The remarkable thing about this compression method is that stochastic gradient descent for some reason creates algorithms in the network. Not Turing-complete algorithms, of course, but algorithms nevertheless. An LLM trained on the addition and multiplication data develops circuits for addition and multiplication[1]. It stands to reason that LLM trained on human-produced data develop algorithms that try to approximate the data production process (within their computational limits). [1] https://arxiv.org/abs/2308.01154&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2308.01154/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
