English(EN) Does the LM Head Create a Harmful Gradient Bottleneck? A Causal Test

Transformer LM 头压缩：有害瓶颈还是几何压缩？

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-18 04:00

研究人员调查了 Transformer 中的语言模型头是否会造成有害的梯度瓶颈。他们使用 WikiText-2 模型进行仅后向干预的实验，发现减小输入 Transformer 的梯度的秩实际上会增加验证损失。相反，具有同等减小秩的因子化前向头会导致损失更大幅度地增加。这些发现表明，虽然发生了强烈的几何压缩，但这可能并非一个有害的优化瓶颈。 AI

影响研究了 Transformer 架构中一个潜在的瓶颈，为模型优化和训练动态提供了见解。

排序理由分析 transformer 模型特定组件的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Anand Murugan · 2026-08-18 04:00

大型语言模型头部是否会造成有害梯度瓶颈？因果检验

arXiv:2608.16671v1 Announce Type: new Abstract: The language-model head maps a hidden state of width D to a vocabulary of size V, so its transpose can return at most D independent directions to the Transformer. Godey and Artzi argue that this severe projection is a harmful optimi…

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大型语言模型头部是否会造成有害梯度瓶颈？因果检验

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