Attention Mechanism in LLMs

Question: What is the role of the attention mechanism in Large Language Models (LLMs)?

Answer: The attention mechanism allows models to focus on specific parts of the input sequence that are most relevant to the task at hand. It is critical for LLMs because:

It dynamically weighs the importance of words in a sequence based on context.
It enables the model to handle long-range dependencies efficiently.
It significantly improves performance in translation, summarization, and text generation tasks.

        
        # Example of scaled dot-product attention
      
        Q = X.dot(W_Q)
      
        K = X.dot(W_K)
      
        V = X.dot(W_V)
      
        attention_scores = Q.dot(K.T) / sqrt(d_k)
      
        attention_weights = softmax(attention_scores)
      
        output = attention_weights.dot(V)