<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A Theory of Emergent In-Context Learning as Implicit Structure Induction · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2303.07971/</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/2303.07971/feed.xml" rel="self" type="application/rss+xml" /><item><title>A Theory of Emergent In-Context Learning as Implicit Structure Induction — mentioned by FeepingCreature</title><link>https://news.ycombinator.com/item?id=35449958</link><guid isPermaLink="false">urn:hackerpapers:mention:2303.07971:35449958</guid><pubDate>Wed, 05 Apr 2023 06:04:10 GMT</pubDate><description>&lt;p&gt;The relevant keyword you want is &amp;quot;zero-shot learning&amp;quot;. (EDIT: Correction; &amp;quot;in-context learning&amp;quot;. Sorry for that.) LLMs can pick up patterns from the context window purely at evaluation time using dynamic reinforcement learning. (This is one of those capabilities models seem to just pick up naturally at sufficient scale.) Those patterns are ephemeral and not persisted to memory, which I agree makes LLMs less general than humans, but that seems a weak objection to hang a fundamental difference in kind on. edit: Correction: I can&amp;#x27;t find a source for my claim that the model specifically picks up…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2303.07971/"&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>
