English(EN) Attention Mean Fields Predict Average Representation Dynamics and Reveal Context-Specific Computation

新方法预测语言模型表征动力学

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了一种名为“注意力均值场”的新方法来分析语言模型表征的动态几何。该方法利用 token 之间的平均注意力来预测表征如何在层与层之间演变，包括在整个语料库的平均演变以及在给定上下文内的特定演变。通过将一个注意力头的实际计算与其预测的均值场进行比较，研究人员可以分离出上下文特定的计算，并理解模型在训练过程中如何依赖于上下文信息。 AI

影响提供了一个新的框架来理解和潜在地控制大型语言模型的内部动力学。

排序理由该集群包含一篇研究论文，详细介绍了一种分析语言模型表征的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Micah Adler, John W. Byers, Mark Crovella · 2026-09-16 04:00

Attention Mean Fields Predict Average Representation Dynamics and Reveal Context-Specific Computation

arXiv:2609.16382v1 Announce Type: cross Abstract: A language model's representation geometry is not predetermined; it evolves as the model runs. A faithful account of that geometry must capture that dynamic process, and so cannot be based solely on model-independent statistics su…

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Attention Mean Fields Predict Average Representation Dynamics and Reveal Context-Specific Computation

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