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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Real-Time Detection of Hallucinated Entities in Long-Form Generation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2509.03531/</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/2509.03531/feed.xml" rel="self" type="application/rss+xml" /><item><title>Real-Time Detection of Hallucinated Entities in Long-Form Generation — mentioned by robrenaud</title><link>https://news.ycombinator.com/item?id=46002110</link><guid isPermaLink="false">urn:hackerpapers:mention:2509.03531:46002110</guid><pubDate>Fri, 21 Nov 2025 07:37:05 GMT</pubDate><description>&lt;p&gt;LLMs do have some internal representations that predict pretty well when they are making stuff up. https://arxiv.org/abs/2509.03531v1 - We present a cheap, scalable method for real-time identification of hallucinated tokens in long-form generations, and scale it effectively to 70B parameter models. Our approach targets \emph{entity-level hallucinations} -- e.g., fabricated names, dates, citations -- rather than claim-level, thereby naturally mapping to token-level labels and enabling streaming detection. We develop an annotation methodology that leverages web search to annotate model…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2509.03531/"&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>
