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Questions and Answers
What core concept did the paper 'Attention is All You Need' introduce for handling long-range dependencies in sequences?
What core concept did the paper 'Attention is All You Need' introduce for handling long-range dependencies in sequences?
- Convolutional Neural Networks (CNNs)
- Attention mechanism (correct)
- Recurrent Neural Networks (RNNs)
- LSTM networks
Which architectural feature distinguishes Transformers from previous models like RNNs in handling long sequences?
Which architectural feature distinguishes Transformers from previous models like RNNs in handling long sequences?
- Pooling layers
- Attention mechanism (correct)
- Dropout layers
- Feedback loops
What aspect of Transformers led to their widespread adoption as the foundation for many NLP models?
What aspect of Transformers led to their widespread adoption as the foundation for many NLP models?
- Incorporation of reinforcement learning
- Dependency on convolutional layers
- Use of transfer learning
- State-of-the-art performance on NLP tasks (correct)
In what way can Transformers be more efficiently parallelized during training compared to RNNs?
In what way can Transformers be more efficiently parallelized during training compared to RNNs?
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Which subsequent NLP models have been built upon the Transformer architecture as mentioned in the text?
Which subsequent NLP models have been built upon the Transformer architecture as mentioned in the text?
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