English(EN) Quantitative Generalization Bounds for Multi-Layer Transformers: A Few Long Strings can Beat Many Short Ones

Transformer泛化理论表明，更长的训练序列具有指数级效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

一篇新发表在arXiv上的研究论文探讨了多层Transformer的泛化能力，重点关注它们如何处理分布内数据以及外推到更长序列。该研究为固定精度Transformer提供了理论保证，将泛化界限与模型参数大小、量化空间和字母表大小联系起来。研究结果表明，增加训练序列长度比增加训练样本数量具有指数级更高的效率，这一预测得到了实验结果的支持。 AI

影响为Transformer扩展提供了理论见解，可能指导未来模型开发以提高效率。

排序理由关于Transformer泛化理论的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Marius Henry, Michael Rizvi-Martel, Michael Hahn · 2026-10-27 04:00

多层Transformer的量化泛化界限：少数长字符串可胜过多数短字符串

arXiv:2610.35715v1 Announce Type: cross Abstract: Transformer-based language models exhibit an astonishing capacity to generalize to longer and more difficult problem instances than those seen in training. However, theoretical understanding of this phenomenon still lags behind. U…

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多层Transformer的量化泛化界限：少数长字符串可胜过多数短字符串

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