what's the background of attention mechanism

注意力机制的背景：

注意力机制是人工智能领域中一种常用的技术，主要应用在自然语言处理（NLP）任务中。它的概念最早源于心理学，被用于研究人类注意力的分配和集中。

在深度学习的应用中，注意力机制被广泛用于序列到序列（seq2seq）模型，例如机器翻译和对话生成等。它的作用是使得模型能够在处理输入序列时动态关注序列中的不同部分，从而提高了模型的效率和准确率。

2015年，注意力机制在论文"Attention Is All You Need"中首次被提出并实际应用。该论文提出了一种全注意力模型，通过使用注意力机制，可以在不使用任何其他结构（例如循环或卷积）的情况下解决机器翻译问题，这一技术也因此得到了广泛关注。

相关问题

what problem does attention mechanism want to solve

注意力机制的目的是解决什么问题？

注意力机制的目的是解决在序列处理任务中信息缺失或忽略的问题。例如，在自然语言处理中，一个语句可能包含很多单词，但并不是所有单词都对当前任务有重要影响。注意力机制可以帮助神经网络在处理序列数据时，更加关注那些对当前任务更重要的部分。

multihead masked attention mechanism

Multi-head masked attention mechanism is a type of attention mechanism used in deep learning models, particularly in transformer-based models like BERT and GPT. It is a variant of the standard attention mechanism used in sequence-to-sequence models.

In multi-head masked attention, the input sequence is split into multiple sub-sequences and each sub-sequence is processed independently using the standard attention mechanism. The output of each sub-sequence is then concatenated and passed through a linear layer to produce the final output.

The "masked" part of the mechanism refers to the fact that during training, some of the input tokens are randomly masked, meaning that they are ignored during the attention calculation. This is done to prevent the model from simply memorizing the input sequence and instead forces it to learn more robust representations.

Overall, multi-head masked attention allows the model to attend to multiple parts of the input sequence simultaneously while also incorporating the concept of masking for improved performance.