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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Explainability for Large Language Models: A Survey · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2309.01029/</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/2309.01029/feed.xml" rel="self" type="application/rss+xml" /><item><title>Explainability for Large Language Models: A Survey — mentioned by _heimdall</title><link>https://news.ycombinator.com/item?id=43302032</link><guid isPermaLink="false">urn:hackerpapers:mention:2309.01029:43302032</guid><pubDate>Sat, 08 Mar 2025 17:58:32 GMT</pubDate><description>&lt;p&gt;This paper [1] may be an interesting place to start. We only know how the structures are designed to work, and we have hypothesise of how they likely work. We can&amp;#x27;t interpret what actually happens when the LLM is actually going through the process of generating a response. That seems pedantic or unimportant on the surface, but there are some really important implications. At the more benign level, we don&amp;#x27;t know why a model gave a bad response when a person wasn&amp;#x27;t happy with the output. On the more important end, any concerns related to the risk of these models becoming self-directed or…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2309.01029/"&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>
