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English(EN) Algebraic Decomposition Theory for Transformer Length Generalization

新理论精确刻画了 Transformer 在正则语言上的长度泛化能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的代数分解理论，用于精确刻画 Transformer 类语言模型在训练长度之外的更长序列上的泛化能力。该理论解决了经典 Krohn-Rhodes 分解理论的局限性，该理论因基本构建块的差异而不足以理解 Transformer 的长度泛化。新方法将分解理论推广到无限群，从而实现了一个用于正则语言成员资格的多项式时间决策算法。实验证实了该理论在预测 Transformer 行为方面的准确性。 AI

影响 为理解和改进 Transformer 的长度泛化能力提供了理论基础。

排序理由 学术论文，详细介绍了用于 AI 模型泛化能力的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新理论精确刻画了 Transformer 在正则语言上的长度泛化能力

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学术论文，详细介绍了用于 AI 模型泛化能力的新理论框架。[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) · Andy Yang, Blerta Veseli, Corentin Barloy, Micha\"el Cadilhac, Andreas Krebs, Charles Paperman, Howard Straubing, Michael Hahn ·

    Transformer长度泛化的代数分解理论

    arXiv:2608.13433v1 Announce Type: cross Abstract: Transformer-based language models are known to sometimes generalize to sequences longer than seen during training, but we lack a precise characterization of which tasks admit length generalization. It is not even known which regul…

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