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Related Questions
- What are the key innovations in the transformer architecture that enable it to handle long-range dependencies in text data?
- How does the self-attention mechanism in transformer models facilitate the handling of long-range dependencies?
- Can you explain the difference in how transformer-based models and traditional RNNs/LSTMs handle sequential data with long-range dependencies?
- What are some common applications where transformer-based models have been shown to outperform traditional models in handling long-range dependencies?
- How do transformer-based models handle the trade-off between capturing long-range dependencies and computational efficiency?
- Can you provide examples of tasks where the limitations of traditional models in handling long-range dependencies result in decreased performance compared to transformer-based models?
- What are some potential areas of research that could lead to further improvements in transformer-based models' ability to handle long-range dependencies in text data?
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