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English(EN) ST$^2$U: Stateful Test-Time Unlearning via Restricted Knowledge Boundary Control

新的ST$^2$U方法在推理过程中控制LLM中的受限知识

研究人员开发了一种名为状态化测试时遗忘(ST$^2$U)的新方法,用于在推理过程中控制大型语言模型中的受限知识。该方法通过实现跨轨迹的边界控制,解决了模型在初步纠正后重新访问不希望信息的问题。ST$^2$U通过将受限知识映射到低维坐标并应用最小的、跨生成token持久存在的修正,显著减少了知识的重新进入,同时保持了模型的能力。 AI

影响 该方法可以通过一种更持久的方式来防止LLM访问或生成不希望的信息,从而增强其安全性和对齐性。

排序理由 该集群包含一篇研究论文,详细介绍了一种控制大型语言模型中知识的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新的ST$^2$U方法在推理过程中控制LLM中的受限知识

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该集群包含一篇研究论文,详细介绍了一种控制大型语言模型中知识的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Xunlei Chen, Qinghui Gong, Ruini Xue, Yaodong Hu, Tian Lan, Wenhong Tian ·

    ST$^2$U:通过受限知识边界控制实现有状态的测试时遗忘

    arXiv:2608.23034v1 Announce Type: cross Abstract: Controlling restricted knowledge in large language models is essential for model alignment and safe deployment. Test-time unlearning avoids costly retraining and parameter updates by intervening only during inference. However, exi…