Self-Attention in LLMs

Question: What is self-attention, and why is it critical in Large Language Models (LLMs)?

Answer: Self-attention is a mechanism that allows a model to dynamically weigh the importance of different words in a sentence based on their relationship to one another. It is critical in LLMs for several reasons:

Captures dependencies between words, regardless of their distance in the text.
Handles complex contextual relationships in sentences.
Computes attention weights that focus on relevant parts of the input sequence.
Provides scalability and parallelization for large datasets.

        
        # Compute self-attention scores
      
        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)