<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" xmlns:media="http://search.yahoo.com/mrss/"><channel><title>Papers on Shaowen Chen's Website</title><link>https://www.chenshaowen.com/en/tags/papers/</link><description>Recent content in Papers on Shaowen Chen's Website</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>&amp;copy;2016 - {year}, All Rights Reserved.</copyright><lastBuildDate>Sat, 05 Jul 2025 00:00:00 +0000</lastBuildDate><sy:updatePeriod>weekly</sy:updatePeriod><atom:link href="https://www.chenshaowen.com/en/tags/papers/atom.xml" rel="self" type="application/rss+xml"/><item><title>AI-Related Papers</title><link>https://www.chenshaowen.com/en/blog/ai-related-papers.html</link><pubDate>Sat, 05 Jul 2025 00:00:00 +0000</pubDate><atom:modified>Sat, 05 Jul 2025 00:00:00 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/ai-related-papers.html</guid><description>2025 Intrinsic Fingerprint of LLMs [Published: 07-04] View
The paper proposes a robust fingerprinting method based on the inter-layer standard-deviation distribution pattern of attention parameter matrices (Q/K/V/O), used to detect lineage relationships between large language models (LLMs) — for example, whether one model was derived from another through continued training, fine-tuning, or an upgrade.</description><dc:creator>微信公众号</dc:creator><category>AI</category><category>Papers</category><category>Storage</category><category>LLM</category></item></channel></rss>