What is the relationship between embeddings and attention mechanisms?

Embeddings and attention mechanisms are complementary components in modern neural networks, particularly in transformer-based models. Embeddings convert discrete inputs like words or tokens into continuous vector representations that capture semantic and syntactic information. Attention mechanisms then use these embeddings to dynamically determine which parts of the input are most relevant for a given task. For example, in a translation task, embeddings represent the meaning of words, while attention identifies which words in the source sentence should influence the translation of a target word. Together, they enable models to process sequential data with context-aware flexibility.

The interaction between embeddings and attention occurs in multiple stages. First, input tokens (e.g., words) are mapped to embedding vectors, which serve as the initial numerical representation of the data. These embeddings are then fed into attention layers, where the model computes similarity scores between pairs of embeddings (e.g., between a query and keys in self-attention). These scores determine how much focus each part of the input receives. For instance, in a sentence like “The cat sat on the mat,” the word “sat” might attend strongly to “cat” and “mat” via attention weights, but this process relies on the embeddings to encode meaningful relationships between the words. Multi-head attention extends this by allowing the model to focus on different aspects of the embeddings simultaneously, such as syntax in one head and semantics in another.

While embeddings and attention are distinct concepts, their synergy is critical. Embeddings provide the foundational representation, while attention refines how these representations interact contextually. A practical example is BERT: its input embeddings combine token, position, and segment information, creating a rich starting point. The attention layers then propagate and refine these embeddings by highlighting dependencies between tokens, such as linking a pronoun to its antecedent in a text. Importantly, embeddings are typically static once trained (for a given input), whereas attention weights are computed dynamically during inference, adapting to each specific input sequence. This separation allows the model to balance fixed semantic knowledge (embeddings) with context-sensitive adjustments (attention), making them jointly essential for tasks requiring both generalization and specificity.