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English(EN) Feed-Forward Steering in Transformer Residual Dynamics

Transformer理论扩展至包含前馈网络

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了Transformer的扩展动力学理论，将前馈网络（FFN）纳入为局部转向场。该新理论表明，FFN的切向分量对于在残差方向空间中的移动至关重要，并且具有小换向器缺陷的层可以并行化。对GPT-2、Pythia、Mistral和Llama模型的实验表明，该扩展理论提高了单步角度预测的准确性，并且FFN的贡献在Llama 3-8B等后期模型中有所增加。干预实验证实，切向FFN分量对于维持模型质量和输出多样性至关重要。 AI

影响 提供了对Transformer架构更深入的理论理解，可能指导未来的模型开发和优化。

排序理由 该集群包含一篇研究论文，详细介绍了理解Transformer模型的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Transformer理论扩展至包含前馈网络

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该集群包含一篇研究论文，详细介绍了理解Transformer模型的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Timur Mudarisov, Mikhail Burtsev, Radu State ·

    Transformer残差动力学中的前馈转向

    arXiv:2608.02071v1 Announce Type: new Abstract: Attention-only dynamical theories model Transformer residual directions as particles aggregating on a sphere. We extend this framework by incorporating the feed-forward network (FFN) term as a local steering field acting on each tok…

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