How do we obtain a context-aware vector in an attention model?

In attention models, a context-aware vector is generated to capture the relationship between the input tokens and help the model focus on the most relevant information. The correct method to obtain this vector involves adding the original embedding vector to the final attention vector.

The original embedding vector represents the initial input information of a particular token, while the attention vector reflects the accumulated contextual information from other tokens, weighted by their relevance. Combining these two vectors allows the model to integrate both the specific characteristics of the token and the contextual insights gained through the attention mechanism. This synergy enhances the model’s ability to understand the nuances within the input sequence.

Other methods mentioned, such as averaging all embedding vectors or selecting the most relevant embedding, do not utilize the full potential of the context provided by attention mechanisms. Using a separate normalization technique might help in certain scenarios but does not directly contribute to generating a context-aware vector in the same integrated way as the addition of vectors does.