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Related Questions
- What are the key differences between transformer-based models and traditional recurrent neural networks in handling long-range dependencies?
- How do transformer-based models learn to capture long-range dependencies in text data, and what techniques are used to facilitate this learning?
- Can you explain the role of self-attention mechanisms in transformer-based models, and how they enable the capture of long-range dependencies?
- What are some common applications of transformer-based models in natural language processing tasks that require long-range dependencies, such as text summarization and machine translation?
- How do transformer-based models perform compared to traditional recurrent neural networks in tasks that require long-range dependencies, such as text summarization and question answering?
- What are some of the challenges and limitations of using transformer-based models for tasks that require long-range dependencies, and how can they be addressed?
- Can you provide an example of a transformer-based model architecture that is well-suited for text summarization tasks that require long-range dependencies?
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