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English(EN) One Vector or Many? Clustered Optimal-Transport Steering under Quantization

新的CHaRS方法引导语言模型减少毒性

研究人员开发了一种新的引导语言模型激活的方法,称为聚类最优传输引导(CHaRS)。该技术使用最优传输来配对类别簇,并根据隐藏状态与这些簇的接近程度来移动它们,提供了添加单个向量的常见做法的一种替代方案。在Gemma-2-2B和Granite-3-1-2B模型上的实验表明,CHaRS,特别是将token路由到最近簇的变体,与标准方法相比,即使在INT8和NF4量化下,也能显著降低毒性。 AI

影响 引入了一种缓解语言模型毒性的新颖技术,有望提高其安全性和可靠性。

排序理由 学术论文,详细介绍了一种新的语言模型激活引导方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的CHaRS方法引导语言模型减少毒性

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学术论文,详细介绍了一种新的语言模型激活引导方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Illia Makarov ·

    一个向量还是多个?量化下的聚类最优传输转向

    arXiv:2610.40055v1 Announce Type: cross Abstract: Activation steering changes what a language model generates by editing its hidden states at inference time. Its most common form adds one vector, the difference of class means (DiM), to every hidden state. CHaRS (concept heterogen…