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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2306.12672/</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/2306.12672/feed.xml" rel="self" type="application/rss+xml" /><item><title>From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought — mentioned by cl42</title><link>https://news.ycombinator.com/item?id=40035348</link><guid isPermaLink="false">urn:hackerpapers:mention:2306.12672:40035348</guid><pubDate>Sun, 14 Apr 2024 23:12:40 GMT</pubDate><description>&lt;p&gt;RAG will have a place in the LLM world, since it&amp;#x27;s a way to obtain data/facts/info for relevant queries. Since you asked about alternatives... (a) &amp;quot;World models&amp;quot; where LLMs structure information into code, structured data, etc. and query those models will likely be a thing. AlphaGeometry uses this[1], and people have tried to abstract this in different ways[2]. (b) Depending on how you define RAG, knowledge graphs could be a form of RAG or alternatively an alternative to them. Companies like Elemental Cognition[3] are building distinct alternatives to RAG that use such graphs and give LLMs…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2306.12672/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought — mentioned by famouswaffles</title><link>https://news.ycombinator.com/item?id=36469352</link><guid isPermaLink="false">urn:hackerpapers:mention:2306.12672:36469352</guid><pubDate>Sun, 25 Jun 2023 15:38:59 GMT</pubDate><description>&lt;p&gt;&amp;gt;But to know that, one has to understand what the data means semantically. And our current AIs ... well, don&amp;#x27;t. Another wrong statement, you&amp;#x27;re on a roll today. https://arxiv.org/abs/2305.11169 https://arxiv.org/abs/2306.12672 There&amp;#x27;s a word we would use to describe your confidently erroneous statements were it one of the outputs an LLM. Wonder what that might be..&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2306.12672/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought — HN submission</title><link>https://news.ycombinator.com/item?id=36445197</link><guid isPermaLink="false">urn:hackerpapers:submission:2306.12672:36445197</guid><pubDate>Fri, 23 Jun 2023 10:57:26 GMT</pubDate><description>&lt;p&gt;From word models to world models&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;100 points; 113 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2306.12672/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
