<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LLM Probing | Yiqiao Jin CS PhD @ Georgia Tech</title><link>https://ahren09.github.io/tags/llm-probing/</link><atom:link href="https://ahren09.github.io/tags/llm-probing/index.xml" rel="self" type="application/rss+xml"/><description>LLM Probing</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sun, 01 Feb 2026 00:00:00 +0000</lastBuildDate><image><url>https://ahren09.github.io/media/icon_hu_eee6347cbdb2cc3f.png</url><title>LLM Probing</title><link>https://ahren09.github.io/tags/llm-probing/</link></image><item><title>Efficient Knowledge Probing of Large Language Models by Adapting Pre-trained Embeddings</title><link>https://ahren09.github.io/publication/aaai26_knowledge_probing/</link><pubDate>Sun, 01 Feb 2026 00:00:00 +0000</pubDate><guid>https://ahren09.github.io/publication/aaai26_knowledge_probing/</guid><description>&lt;h2 id="abstract">Abstract&lt;/h2>
&lt;p>Probing what a large language model knows is essential for safe deployment, but exhaustive probing is prohibitively expensive. We propose an efficient knowledge probing approach that adapts pre-trained embeddings to query LLM knowledge with substantially reduced compute, while preserving the fidelity of standard probing protocols.&lt;/p>
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