Welcome to understanding the Attention mechanism in Large Language Models. The Attention mechanism is a revolutionary approach that allows models to dynamically focus on relevant parts of the input sequence. Unlike traditional sequential models that process information step by step, Attention enables direct connections between any two positions in the sequence, allowing the model to understand context more effectively.
The first step in the Attention mechanism is generating three types of vectors for each input element. The Query vector represents what information the current element is looking for. The Key vector represents what information the element contains. The Value vector carries the actual information content. These vectors are created by multiplying the input embedding with learned weight matrices W_Q, W_K, and W_V respectively.
The next step is computing attention scores to determine how much focus each element should receive. We calculate the dot product between the Query vector and each Key vector. These raw scores are then scaled by the square root of the key dimension to prevent gradients from becoming too small. Finally, we apply the softmax function to convert scores into probabilities that sum to one, creating the attention weights.
The final step in the attention mechanism is computing a weighted sum of all Value vectors using the attention weights we just calculated. Each Value vector is multiplied by its corresponding attention weight, and all these weighted values are summed together. This produces a context vector that represents a focused, weighted combination of information from the entire input sequence, emphasizing the most relevant parts for the current query.
Attention机制是现代深度学习中的一项重要技术，特别是在自然语言处理领域。它的核心思想是让模型能够动态地关注输入序列中的不同部分，而不是对所有信息一视同仁。就像人类阅读时会重点关注某些关键词汇一样，Attention机制帮助模型识别和聚焦于最相关的信息。
Attention机制基于Query、Key、Value三元组概念。可以把它想象成一个查询系统：Query是你要查找的问题，Key是每个位置的索引标签，Value是实际存储的信息。计算过程分为三步：首先Query与所有Key计算相似度得到注意力分数，然后将分数归一化得到注意力权重，最后用这些权重对所有Value进行加权求和，得到最终的输出。
Attention机制的核心计算公式包含四个步骤。首先计算Query和Key的转置矩阵相乘，得到相似度分数。然后除以Key向量维度的平方根进行缩放，这样可以防止梯度消失。接着应用softmax函数将分数归一化为概率分布。最后用得到的注意力权重与Value矩阵相乘，得到加权后的输出。这个过程确保了模型能够根据相关性动态分配注意力。
Self-Attention是Attention机制的一种特殊应用，其中Query、Key和Value都来源于同一个输入序列。在这种机制下，序列中的每个位置都会关注序列中的所有其他位置，包括它自己。这种全连接的注意力模式使得模型能够捕获长距离的依赖关系，即使是相距很远的词汇也能建立直接的联系。Self-Attention的另一个优势是可以并行计算，大大提高了训练效率。
Multi-Head Attention是一个强大的扩展，它并行运行多个注意力机制。每个注意力头都学习关注词汇之间关系的不同方面，使用不同的学习权重矩阵来进行Query、Key和Value的变换。在独立计算注意力后，所有头的输出被拼接在一起，然后通过最终的线性变换。这使得模型能够同时关注不同类型的语言关系，比单头注意力机制更具表达能力。