自注意力计算流程中的scale的作用

英文回答：
In theputational process of self-attention, the scale factor plays
a pivotal role in determining the significance of various words
or tokens within a sequence. The scale factor is employed to standardize the dot products of the query and key vectors, thereby preventing the gradient of the softmax function from diminishing as the input dimensionality increases. The absence
of a scale factor could lead to excessively large dot products between the query and key vectors as the dimensionality grows, resulting in minute gradients during backpropagation. This phenomenon may impede or destabilize the training process. Consequently, the scale factor serves to ensure that the attention scores for different tokens are suitably adjusted to a reasonable range by scaling down the dot products according
to the input dimensionality.
在自留心的截图过程中，比例因素在确定序列内各种词或符号的意义
方面起着关键作用。

尺度因子用于将查询和关键向量的点产品标准化，从而防止软max函数的梯度随着输入维度的增加而降低。

没有尺度系数，可能导致在查询和关键矢量之间产生过多的点产物，因为维度增加，在反射过程中产生微小梯度。

这种现象可能妨碍或动摇培训进程。

比额表系数有助于通过根据输入维度缩小点产品的规模，确保不同符
号的注意分数适当调整到合理的范围。

Furthermore, the scale factor also helps to control how big the output of the softmax function is, which then affects how much each word's attention score influences the final result. Using a scale factor allows the model to manage the average size of attention weights, making training more stable and improving how we understand the attention process. Plus, the scale factor can also help the training process run more smoothly by keeping the gradients from getting too small, which can slow down learning. Overall, the scale factor in the self-attention process is really important for making sure neural network training is steady and effective.
比例系数还有助于控制软max函数的输出量有多大，从而影响每个词的注意力分数对最终结果的影响程度。

使用尺度因子可以使模型管理
关注权重的平均大小，使培训更加稳定，并改进我们对关注过程的理解。

比例系数还可以通过防止梯度变小来帮助训练过程更顺利地进行，这可以减缓学习速度。

总体而言，自觉过程的尺度因素对于确保神经
网络训练的稳定性和有效性非常重要。

In conclusion, the inclusion of the scale factor within the self-attention calculation process is deemed imperative for the
normalization of dot products affiliated with the query and key vectors. This scale factor serves the critical function of regulating the magnitude of attention weights while concurrently forestalling the occurrence of diminishing gradients during the backpropagation process. Through the deliberate utilization of the scale factor, the model is able to effectively govern the impact of attention scores, thereby ensuring the stable and efficient training of neural network models. Consequently, it is evident that the scale factor represents a fundamental constituent within the self-attention mechanism, actively contributing to theprehensive performance and effectiveness of neural network models when they are processing sequential data.
将比例系数纳入自觉计算过程被认为是与查询和关键矢量有关的点产
品正常化的必要条件。

这一尺度系数在调节注意力权重的大小同时防
止在反向传播过程中发生梯度下降这一关键功能。

通过刻意利用尺度
因子，该模型能够有效支配注意力分数的影响，从而确保神经网络模
型的稳定高效训练。

很明显，尺度系数是自觉机制中的一个基本组成
部分，在神经网络模型处理顺序数据时积极促进其综合性能和有效性。