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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.
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# Example of scaled dot-product attention
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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)