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Related Questions
- How does the relative position encoding scheme compare to the fixed position encoding scheme in terms of capturing long-range dependencies?
- Can you explain the difference between absolute and relative positional encoding schemes and how they impact the model's ability to capture long-range dependencies?
- How does the choice of positional encoding scheme influence the model's ability to learn hierarchical structures and relationships between tokens?
- What are the trade-offs between using learned positional encoding schemes versus fixed schemes, and how do they affect the model's ability to capture long-range dependencies?
- How does the positional encoding scheme impact the model's performance on tasks that require long-range dependency modeling, such as language modeling and machine translation?
- Can you provide examples of scenarios where the choice of positional encoding scheme makes a significant difference in the model's ability to capture long-range dependencies?
- How can the model's ability to capture long-range dependencies be evaluated and compared across different positional encoding schemes?
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