<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Prefix-Tuning on Chen Kai Blog</title><link>https://www.chenk.top/zh/tags/prefix-tuning/</link><description>Recent content in Prefix-Tuning on Chen Kai Blog</description><generator>Hugo</generator><language>zh-CN</language><lastBuildDate>Tue, 29 Jul 2025 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/zh/tags/prefix-tuning/index.xml" rel="self" type="application/rss+xml"/><item><title>Prefix-Tuning：为生成任务优化连续提示</title><link>https://www.chenk.top/zh/standalone/prefix-tuning-optimizing-continuous-prompts-for-generation/</link><pubDate>Tue, 29 Jul 2025 09:00:00 +0000</pubDate><guid>https://www.chenk.top/zh/standalone/prefix-tuning-optimizing-continuous-prompts-for-generation/</guid><description>&lt;p>将 GPT-2 微调到具体任务上需要额外存储 1.5B 参数的权重；切换十几个任务时，存储和上线成本会让团队望而却步，更不用说实现“一份基模 + 多任务共享”的理想架构。&lt;strong>Prefix-Tuning&lt;/strong>（Li &amp;amp; Liang, 2021）走了一条相反的路：模型权重一个不动，只学一小段连续向量——也就是论文里所说的“前缀”——在每一层注意力里被当作“已经在那里的上下文”喂进去。模型本身保持不变，只需更换前缀，即可赋予模型对应任务的适配行为。&lt;/p></description></item></channel></rss>