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English(EN) Disentangling Representation Evolution in Transformers through Directional Decomposition

新方法解耦 Transformer 表示演变

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种方法，通过将学习到的更新分解为平行和垂直分量来解耦 Transformer 模型中的表示演变。该分析揭示，值空间中的平行操作比其他方法更鲁棒，它在缩放非自聚合的同时保留了直接的自消息。这种分解还有助于诊断由压缩引起的错误，并表明在预训练期间抑制完全聚合的平行更新可以提高模型性能并降低验证损失。 AI

影响 为理解和潜在改进 Transformer 模型行为和训练提供了一个新的分析框架。

排序理由 学术论文，详细介绍了一种分析和干预 Transformer 模型的新方法。[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 →

新方法解耦 Transformer 表示演变

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学术论文，详细介绍了一种分析和干预 Transformer 模型的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Shwai He, Haichao Zhang, Shen Yan ·

    通过方向分解解耦 Transformer 中的表征演化

    arXiv:2609.15975v1 Announce Type: new Abstract: Transformer representations evolve through learned additive transformations that either preserve their current direction or redirect it. We study this evolution as a functional geometry, decomposing learned updates into parallel and…

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