<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Analyzing the Structure of Attention in a Transformer Language Model · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1906.04284/</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/1906.04284/feed.xml" rel="self" type="application/rss+xml" /><item><title>Analyzing the Structure of Attention in a Transformer Language Model — mentioned by versteegen</title><link>https://news.ycombinator.com/item?id=34672937</link><guid isPermaLink="false">urn:hackerpapers:mention:1906.04284:34672937</guid><pubDate>Mon, 06 Feb 2023 03:25:01 GMT</pubDate><description>&lt;p&gt;See my reply to sibling: https://news.ycombinator.com/item?id=34672865 A more interesting example of transformers learning a process may be [1]. There&amp;#x27;s a large literature on applying language models to reasoning tasks, but not many on what&amp;#x27;s actually going on inside them. But see for example [2]. Also https://transformer-circuits.pub/ has a body of work on it, but still at a very early stage (see in particular &amp;quot;In-context Learning and Induction Heads&amp;quot;). [1] Extraction of organic chemistry grammar from unsupervised learning of chemical reactions…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1906.04284/"&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>
