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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.
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# Compute self-attention scores
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Q = X.dot(W_Q)
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K = X.dot(W_K)
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V = X.dot(W_V)
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attention_scores = Q.dot(K.T) / sqrt(d_k)
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attention_weights = softmax(attention_scores)
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output = attention_weights.dot(V)