📄 中文摘要
自注意力机制通常被描述为“令牌相互关注”。然而，其内部实际上是一个精确的两步机制。首先，模型为当前令牌计算序列中每个其他令牌的重要性得分。这一过程称为相关性评分。通过这种方式，模型能够评估不同令牌之间的关系，从而在处理信息时更加高效和准确。自注意力机制在自然语言处理和其他领域中发挥着关键作用，帮助模型理解上下文和语义。
自注意力机制内部究竟发生了什么？
出处: 🔷 What Actually Happens Inside Self-Attention?
发布: 2026年2月15日
📄 English Summary
🔷 What Actually Happens Inside Self-Attention?
Self-attention is often described as 'tokens looking at each other.' However, it operates through a precise two-step mechanism. First, the model computes relevance scores for the current token, assessing how important every other token in the sequence is. This relevance scoring allows the model to evaluate relationships between tokens, enhancing efficiency and accuracy in information processing. Self-attention plays a crucial role in natural language processing and other fields, aiding models in understanding context and semantics.