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Related Questions
- What is the primary factor that affects a transformer model's ability to process long-range dependencies?
- How does the self-attention mechanism in transformer models enable them to handle long-range dependencies?
- What is the difference between capacity and computational cost in the context of transformer models?
- In what ways does a higher capacity transformer model improve its performance on tasks involving long-range dependencies?
- Can you provide examples of tasks where long-range dependencies are crucial, and how transformer models can benefit from higher capacity?
- How does the concept of capacity relate to the architecture of transformer models, particularly in terms of layer stacking and embedding dimensions?
- What are the limitations of current transformer models in handling extremely long-range dependencies, and how might these be addressed through advances in capacity?
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