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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2303.13284/</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/2303.13284/feed.xml" rel="self" type="application/rss+xml" /><item><title>GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering — mentioned by qayxc</title><link>https://news.ycombinator.com/item?id=36452631</link><guid isPermaLink="false">urn:hackerpapers:mention:2303.13284:36452631</guid><pubDate>Fri, 23 Jun 2023 21:51:35 GMT</pubDate><description>&lt;p&gt;&amp;gt; Do we even know why AIs &amp;quot;hallucinate&amp;quot;? It&amp;#x27;s because, just like human memory, they aren&amp;#x27;t databases or search engines. Generative transformer models are basically next-word-prediction machines on steroids. They take the input and try to &amp;quot;guess&amp;quot; the most likely reply based on their training data. These machines have no way to distinguish facts from fiction, only probabilities of combinations of words that would make the most plausible reply. &amp;gt; Is it possible to prevent it? There are methods to prevent this by incorporating specialised knowledge databases into the training material of these…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2303.13284/"&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>
