斯坦福新论文：按提示词动态调节安全对齐，减少性能损失
Safety alignment usually costs utility because the aligned weights apply to ever…
Safety alignment usually costs utility because the aligned weights apply to every prompt;
安全对齐通常以牺牲实用性为代价，因为对齐后的权重适用于每个提示词；
New Stanford univ paper finds that scaling the safety update per prompt recovers much of what global tuning gives up.
斯坦福大学的新论文发现，按提示词缩放安全更新，可以恢复全局调优所放弃的大部分性能。
The problem is that a safety fine-tune changes the model for every input. Harmful or not, every prompt now runs through a safer but weaker model.
问题在于，安全微调会改变模型对每个输入的处理方式。无论是否有害，每个提示词现在都通过一个更安全但更弱的模型运行。
CLEAR, proposed in this paper, leaves the original model frozen. A small gate reads each incoming prompt and decides how much of a separate safety module to switch on.
本文提出的CLEAR方法保持原始模型不变。一个小型门控读取每个传入的提示词，并决定开启多少独立的安全模块。
Benign prompts get almost none of it, so they run on the untouched model.
良性提示词几乎不会触发该模块，因此它们运行在未修改的模型上。
– arxiv. org/abs/2608.21278
– arxiv.org/abs/2608.21278
Title: "CLEAR: Continuous Latent Adapter Routing for Utility-Preserving LLM Safety Alignment"
标题：“CLEAR：用于保持实用性的LLM安全对齐的连续潜在适配器路由”
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