<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Large Language Models | Farhan Noor Dehan</title><link>https://dehan001.github.io/tags/large-language-models/</link><atom:link href="https://dehan001.github.io/tags/large-language-models/index.xml" rel="self" type="application/rss+xml"/><description>Large Language Models</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 08 Dec 2025 00:00:00 +0000</lastBuildDate><image><url>https://dehan001.github.io/media/icon_hu7729264130191091259.png</url><title>Large Language Models</title><link>https://dehan001.github.io/tags/large-language-models/</link></image><item><title>Evaluating the Few-Shot Performance of Large Language Models for Classifying Clinical Risk Factors in Mental Health Text</title><link>https://dehan001.github.io/publication/few-shot-clinical-risk/</link><pubDate>Mon, 08 Dec 2025 00:00:00 +0000</pubDate><guid>https://dehan001.github.io/publication/few-shot-clinical-risk/</guid><description>&lt;p>This study benchmarks Gemini, GPT, Llama, and Mistral across three binary classification tasks involving family abuse, substance use, and prior suicide attempts.&lt;/p></description></item></channel></rss>