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English(EN) A Hub of Short Rows Inflates Intrinsic Dimension Estimation of Token Embeddings

新研究揭示了词嵌入的几何偏差及其对语言模型训练的影响

作者 PulseAugur 编辑部 · [3 个来源] ·

三篇新的arXiv论文探讨了语言模型中词嵌入的几何和统计特性。第一篇论文识别出词嵌入表中靠近原点的“短行中心”，该中心膨胀了内在维度估计，并表明移除该中心可以使GPT-2、K3和GLM-4.7等模型获得更一致的维度读数。第二篇论文引入了“上下文阶梯”概念，描述了嵌入在训练过程中如何逐步学习到更复杂、依赖于上下文的统计特征。第三篇论文开发了一个将词预测与表示几何联系起来的统计框架，展示了预测准确性和恢复的几何如何转化为下游任务性能。 AI

影响 这些论文提供了对语言模型如何学习表示的更深入理解，可能指导未来的架构和训练改进。

排序理由 该集群包含三篇在arXiv上发表的学术论文，详细介绍了关于语言模型中词嵌入的理论发现。

在 arXiv cs.CL 阅读 →

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新研究揭示了词嵌入的几何偏差及其对语言模型训练的影响

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该集群包含三篇在arXiv上发表的学术论文，详细介绍了关于语言模型中词嵌入的理论发现。
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报道来源 [3]

  1. arXiv cs.CL TIER_1 English(EN) · Alexandre Quemy ·

    短行集线器膨胀了Token嵌入的内在维度估计

    arXiv:2608.29702v1 Announce Type: new Abstract: A token-embedding table holds a hub of short rows near its origin, and we show that this cluster biases what nearest-neighbor intrinsic-dimension (ID) estimators report. Because of the concentration of measure, a token is closer to …

  2. arXiv cs.CL TIER_1 English(EN) · Junjie Yao, Liangkai Hang, Zhi-Qin John Xu ·

    Context Staircase: Token Embeddings under Small Initialization的Signature-Aligned Dynamics

    arXiv:2608.30315v1 Announce Type: cross Abstract: Token embeddings are the basic representational units that connect discrete tokens with continuous computation in language models. Although modern language models learn embeddings from random initialization through gradient-based …

  3. arXiv cs.LG TIER_1 English(EN) · Shulei Wang ·

    通过令牌预测学习表示：几何、近似和下游保证

    arXiv:2608.30072v1 Announce Type: cross Abstract: Token prediction is a central pre-training objective for modern language models. Despite its empirical success, why token prediction learns broadly useful representations remains incompletely understood. We develop a statistical f…

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