English(EN) Measuring Optimal Transport in Transformer Depth

AI 模型中的 token 移动与最优传输理论在最后几层对齐

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

一篇新的研究论文探讨了 Transformer 模型中 token 状态的移动，并将其与最优传输理论进行比较。该研究分析了 Pythia-160M 和 Pythia-410M 模型，发现在最后一层，token 通常以接近最优的成本移动到其最优目的地。然而，这种对齐在初始层中不太明显，并且随着模型的训练而显著提高。 AI

影响为理解 Transformer 模型内部工作机制提供了见解，可能为未来的架构改进提供信息。

排序理由在 arXiv 上发表的研究论文，详细介绍了 Transformer 模型行为的发现。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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

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arXiv cs.CL TIER_1 English(EN) · Alexandre Quemy · 2026-09-02 04:00

Measuring Optimal Transport in Transformer Depth

arXiv:2609.00748v1 Announce Type: new Abstract: A transformer carries each token's state from layer to layer, and the whole vocabulary carried together forms a cloud that moves with depth. We ask whether a trained network moves this cloud the way optimal transport would: at the c…

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Measuring Optimal Transport in Transformer Depth

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