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Related Questions
- What are the main computational costs associated with self-attention mechanisms in transformers, and how do they impact model scalability?
- How do the limitations of self-attention in handling long-range dependencies affect its performance in certain NLP tasks, and what alternative approaches have been proposed?
- What are the primary reasons behind the vanishing gradients problem in transformers, and how are researchers addressing this issue through techniques such as reversible transformations?
- In what ways do transformers struggle with handling out-of-vocabulary words and how are current research efforts addressing this challenge?
- How do the limitations of transformer-based models in capturing complex long-range relationships and hierarchies impact their performance in tasks like question answering and document summarization?
- What are the trade-offs between the model size, training speed, and performance of transformer-based models, and how are researchers balancing these factors in current research?
- Can you explain the limitations of standard self-attention in handling sequential data with variable length, and how have researchers proposed modifications to address this issue?
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