<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" xmlns:media="http://search.yahoo.com/mrss/"><channel><title>Technology Selection on Shaowen Chen's Website</title><link>https://www.chenshaowen.com/en/tags/technology-selection/</link><description>Recent content in Technology Selection on Shaowen Chen's Website</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>&amp;copy;2016 - {year}, All Rights Reserved.</copyright><lastBuildDate>Sat, 19 Aug 2023 00:00:00 +0000</lastBuildDate><sy:updatePeriod>weekly</sy:updatePeriod><atom:link href="https://www.chenshaowen.com/en/tags/technology-selection/atom.xml" rel="self" type="application/rss+xml"/><item><title>Technical Factors That Affect How You Use Large Models</title><link>https://www.chenshaowen.com/en/blog/the-key-factors-while-using-large-models.html</link><pubDate>Sat, 19 Aug 2023 00:00:00 +0000</pubDate><atom:modified>Sat, 19 Aug 2023 00:00:00 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/the-key-factors-while-using-large-models.html</guid><description>1. What a Large Model Actually Is First, let us ask two large models this question and see what they answer.
Claude says that a large model is essentially a probabilistic expression of linguistic knowledge: through statistical learning it models the regularities at every level of language and represents the prior distribution of language generation, thereby gaining the ability to predict and generate language.</description><dc:creator>WeChat Official Account</dc:creator><category>AI</category><category>LLM</category><category>Thoughts</category><category>Learning</category><category>Technology Selection</category></item></channel></rss>