<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LLM Risks on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/llm-risks/</link><description>Recent content in LLM Risks on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/llm-risks/index.xml" rel="self" type="application/rss+xml"/><item><title>Hallucination</title><link>https://terms-en.ai-term-hub.com/en/terms/hallucination/</link><pubDate>Sat, 18 Jul 2026 07:39:00 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/hallucination/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Hallucinations occur when generative AI models produce output that appears plausible but lacks grounding in reality or source data. This is a significant challenge in applications requiring high accuracy, such as healthcare or law. The model predicts likely next tokens based on patterns rather than verifying facts, leading to fabricated citations, false statements, or logical inconsistencies that users must carefully validate.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>When an AI model generates confident but factually incorrect or nonsensical information.&lt;/p></description></item></channel></rss>