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What are the key findings about transformer attention mechanisms?
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Based on the uploaded paper, the key findings are: (1) Self-attention allows the model to weigh the importance of different parts of the input, (2) Multi-head attention enables the model to jointly attend to information from different representation subspaces, (3) The attention mechanism achieves O(n²) complexity but remains highly efficient in practice.
Source: paper_v2.pdf · Page 3
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How does this compare to RNN-based approaches?
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