<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LLMs on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/llms/</link><description>Recent content in LLMs 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/llms/index.xml" rel="self" type="application/rss+xml"/><item><title>Mixture of Experts</title><link>https://terms-en.ai-term-hub.com/en/terms/moe/</link><pubDate>Sat, 18 Jul 2026 10:07:54 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/moe/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Mixture of Experts (MoE) is a machine learning architecture designed to improve efficiency and scalability. Instead of using a single large model for all tasks, MoE employs multiple smaller &amp;rsquo;expert&amp;rsquo; networks, each specialized in different aspects of the data. A trainable gating network determines which experts should handle specific inputs, allowing the model to activate only a subset of parameters for each token. This sparsity enables significantly larger model capacity with reduced computational cost during inference, making it ideal for large-scale language models.&lt;/p></description></item><item><title>Claude</title><link>https://terms-en.ai-term-hub.com/en/terms/claude/</link><pubDate>Sat, 18 Jul 2026 09:40:12 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/claude/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Claude is a series of advanced large language models created by the AI safety company Anthropic. Known for its strong alignment principles and constitutional AI framework, Claude focuses on being helpful, harmless, and honest. It excels in natural language understanding, coding assistance, and long-context processing. Unlike some competitors, Claude emphasizes safety and ethical considerations in its design, aiming to reduce harmful outputs while maintaining high utility for complex tasks such as analysis, creative writing, and software development.&lt;/p></description></item></channel></rss>