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  "thinking": "Okay, so I need to explain how transformer attention works. Hmm, where do I start? I remember that transformers are used in neural networks, especially for tasks like translation or generating text. But the part about attention is a bit fuzzy.\n\nWait, I think it's something about paying attention to different parts of the input when processing each word. Like, each word doesn't just look at all words but focuses on relevant ones. That must be how context is handled more effectively than in previous models like RNNs or LSTMs.\n\nSo, maybe the first thing to explain is what self-attention is. I recall that it's a mechanism where the model can attend to different positions in the input sequence based on learned parameters. Each word generates a query, key, and value vector. Then, for each position, the model computes attention scores using these vectors.\n\nBut how exactly do those scores get computed? Oh right, by taking the dot product of the query with each key vector. But since queries can be long, this might lead to high computational costs because it's O(n²) where n is the sequence length. So there must be a way to reduce that complexity.\n\nAh, I think they use scaled dot-product attention and also apply a softmax function",
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