<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hallucination | Yiqiao Jin CS PhD @ Georgia Tech</title><link>https://ahren09.github.io/tags/hallucination/</link><atom:link href="https://ahren09.github.io/tags/hallucination/index.xml" rel="self" type="application/rss+xml"/><description>Hallucination</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 01 Jun 2026 00:00:00 +0000</lastBuildDate><image><url>https://ahren09.github.io/media/icon_hu_eee6347cbdb2cc3f.png</url><title>Hallucination</title><link>https://ahren09.github.io/tags/hallucination/</link></image><item><title>MedHalu: Hallucinations in Responses to Healthcare Queries by Large Language Models</title><link>https://ahren09.github.io/publication/icwsm26_medhalu/</link><pubDate>Mon, 01 Jun 2026 00:00:00 +0000</pubDate><guid>https://ahren09.github.io/publication/icwsm26_medhalu/</guid><description>&lt;h2 id="abstract">Abstract&lt;/h2>
&lt;p>Large language models are increasingly used for consumer healthcare queries, but their responses can contain subtle hallucinations with serious implications for patient safety. We introduce MedHalu, a benchmark for studying hallucinations in LLM responses to healthcare queries, with fine-grained annotations of hallucination types and severity.&lt;/p>
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