What is self-attention in AI?
Self-attention is a mechanism that allows models to weigh the importance of different words in a sequence when processing them.
How does self-attention improve natural language processing?
It helps models understand context better by focusing on relevant words, leading to improved comprehension and generation.
Can you explain how self-attention works mathematically?
Sure! Self-attention computes a score for each word in relation to others, using dot products and softmax to create attention weights.
What are some applications of self-attention?
It's widely used in models like Transformers for tasks such as translation, summarization, and sentiment analysis.
Are there any limitations to self-attention?
Yes, it can be computationally expensive and may struggle with very long sequences due to memory constraints.
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