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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2403.08462/</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/2403.08462/feed.xml" rel="self" type="application/rss+xml" /><item><title>Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification — mentioned by spindump8930</title><link>https://news.ycombinator.com/item?id=47783530</link><guid isPermaLink="false">urn:hackerpapers:mention:2403.08462:47783530</guid><pubDate>Wed, 15 Apr 2026 18:53:05 GMT</pubDate><description>&lt;p&gt;The article seems quite editorialized, shifting between describing &amp;quot;large-scale AI models&amp;quot; and &amp;quot;neural network-based approaches&amp;quot;. The underlying paper itself is more precise, comparing against LUAR, a 2021 method based on bert-style embeddings (i.e. a model with 82M parameters, which is 0.2% the size of e.g. the recent OS Gemma models). I don&amp;#x27;t fault the authors of the paper at all for this, their method is interesting and more interpretable! But you can check the publication history, their paper was uploaded originally in 2024: https://arxiv.org/abs/2403.08462 A good example of why some…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2403.08462/"&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>
