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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LLMs Will Always Hallucinate, and We Need to Live With This · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2409.05746/</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/2409.05746/feed.xml" rel="self" type="application/rss+xml" /><item><title>LLMs Will Always Hallucinate, and We Need to Live With This — mentioned by gozucito</title><link>https://news.ycombinator.com/item?id=49762260</link><guid isPermaLink="false">urn:hackerpapers:mention:2409.05746:49762260</guid><pubDate>Sat, 19 Sep 2026 00:53:59 GMT</pubDate><description>&lt;p&gt;It is surprising to me that recursive self-improvement seems more plausible now than it did in 2023. Am I the only one to be surprised? I remember the paper proving that hallucinations could never be fully solved back in 2024: https://arxiv.org/abs/2409.05746 I also remember the hang-wringing about running out of new datasets to train on. Now it appears humans are always generating more data. It&amp;#x27;s just not as cheap to acquire as legacy data? Meta has to give a deep discount on their API prices to entice people. I thought back then that humans had a few more breakthroughs in them as meaningful…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2409.05746/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>LLMs Will Always Hallucinate, and We Need to Live With This — mentioned by ezst</title><link>https://news.ycombinator.com/item?id=44349345</link><guid isPermaLink="false">urn:hackerpapers:mention:2409.05746:44349345</guid><pubDate>Sun, 22 Jun 2025 18:52:39 GMT</pubDate><description>&lt;p&gt;&amp;gt; Still struggling to find anything in your wall of words that actually backs up your claims. I mean, one easy thing you could do is to explain how LLMs are adequate as &amp;gt;&amp;gt; thinking machines, search engines, knowledge archives, … How much practical experience do you have with LLMs? Have you even tried running models locally? Then how have you not experienced yourself tweaking few parameters and gotten the same model to say one thing and its opposite just after? Through your extensive use of commercial LLMs, aren&amp;#x27;t you getting daily occurrences of confabulations? Making stuff up is what…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2409.05746/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>LLMs Will Always Hallucinate, and We Need to Live With This — mentioned by Retr0id</title><link>https://news.ycombinator.com/item?id=41997587</link><guid isPermaLink="false">urn:hackerpapers:mention:2409.05746:41997587</guid><pubDate>Wed, 30 Oct 2024 17:06:43 GMT</pubDate><description>&lt;p&gt;Much like you can search the internet until you find a source that agrees with you, you can select a set of papers that &amp;quot;confirm&amp;quot; a particular viewpoint, especially in developing fields of research. In this case, the selected papers all support the view LLMs &amp;quot;know what they know&amp;quot; on some internal level, which iiuc is not (yet?) a consensus viewpoint (from my outsider perspective). But from the list alone, you might get that impression. Related: Still no lie detector for language models: probing empirical and conceptual roadblocks - https://link.springer.com/article/10.1007/s11098-023-02094-3…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2409.05746/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>LLMs Will Always Hallucinate, and We Need to Live With This — mentioned by az09mugen</title><link>https://news.ycombinator.com/item?id=41876720</link><guid isPermaLink="false">urn:hackerpapers:mention:2409.05746:41876720</guid><pubDate>Fri, 18 Oct 2024 06:03:25 GMT</pubDate><description>&lt;p&gt;On what basis you think you will avoid hallucination(s) ? LLM will always hallucinate. People did already some research, among which : https://arxiv.org/abs/2409.05746 and https://arxiv.org/abs/2401.11817 for example.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2409.05746/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>LLMs Will Always Hallucinate, and We Need to Live With This — mentioned by az09mugen</title><link>https://news.ycombinator.com/item?id=41763766</link><guid isPermaLink="false">urn:hackerpapers:mention:2409.05746:41763766</guid><pubDate>Mon, 07 Oct 2024 08:05:50 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;m not overthinking it, some papers describe it [0] and [1] for example. I agree for some subjects you can deal with some error. The problem is not how often but how bad just one single error can be. My point was on controversial topic, where a single error can deal serious damage. Yes it must be error-free like for a nuclear reactor. Just imagine a Q&amp;amp;A chatbot answering questions on the subject of Israel and Palestine or something else really touchy, do you really think you can afford any error/hallucination ? [0] : https://arxiv.org/abs/2409.05746 [1] : https://arxiv.org/abs/2401.11817&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2409.05746/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>LLMs Will Always Hallucinate, and We Need to Live With This — mentioned by az09mugen</title><link>https://news.ycombinator.com/item?id=41611279</link><guid isPermaLink="false">urn:hackerpapers:mention:2409.05746:41611279</guid><pubDate>Sat, 21 Sep 2024 17:13:03 GMT</pubDate><description>&lt;p&gt;Since the first day I consider AI as a noob working very fast and always making errors. My main usage is for tedious and non-critical tasks such as boilerplates. And I always review what has been generated like a merge/pull request, because in a sense that&amp;#x27;s what it is. What reinforces my opinion are papers like these : https://arxiv.org/abs/2409.05746 https://arxiv.org/abs/2401.11817&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2409.05746/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>LLMs Will Always Hallucinate, and We Need to Live With This — HN submission</title><link>https://news.ycombinator.com/item?id=41541053</link><guid isPermaLink="false">urn:hackerpapers:submission:2409.05746:41541053</guid><pubDate>Sat, 14 Sep 2024 17:02:56 GMT</pubDate><description>&lt;p&gt;LLMs Will Always Hallucinate, and We Need to Live with This&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;291 points; 261 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2409.05746/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>LLMs Will Always Hallucinate, and We Need to Live With This — HN submission</title><link>https://news.ycombinator.com/item?id=41519163</link><guid isPermaLink="false">urn:hackerpapers:submission:2409.05746:41519163</guid><pubDate>Thu, 12 Sep 2024 09:54:26 GMT</pubDate><description>&lt;p&gt;LLMs Will Always Hallucinate, and We Need to Live with This&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;4 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2409.05746/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
