In the context of natural language processing, the attention mechanism used in Transformer models and the process of converting tokens to vectors and calculating cosine similarity have similarities but serve different purposes.
When you convert words (tokens) into vectors and calculate cosine similarity, you're typically doing what's called "word embedding". This process captures the semantic meaning of words in a high-dimensional space. Words that have similar meanings have vectors that are close to each other in this space. Cosine similarity is a measure of how similar two vectors are, which in this context equates to how similar the meanings of two words are.
On the other hand, the attention mechanism in Transformer models is a way to understand the relationships between words within a specific context. It determines how much each word in a sentence contributes to the understanding of every other word in the sentence. It's not just about the semantic similarity of words, but also about their grammatical and contextual relationships in the given sentence.
Here's an analogy: imagine you're trying to understand a conversation between a group of friends. Just knowing the meaning of their words (like word embeddings do) can help you understand some of what they're saying. But to fully understand the conversation, you also need to know who's speaking to whom, who's agreeing or disagreeing with whom, who's changing the topic, and so on. This is similar to what the attention mechanism does: it tells the model who's "talking" to whom within a sentence.
So while word embeddings and cosine similarity capture static word meanings, the attention mechanism captures dynamic word relationships within a specific context. Both are important for understanding and generating human language.