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Related Questions
- How does the number of transformer layers affect a language model's ability to reason over long-range dependencies in a sequence?
- What is the impact of increasing the model's embedding size on its capacity to capture long-range relationships in a sequence?
- Can you explain how the attention mechanism helps a model to focus on relevant parts of the input sequence when reasoning over long-range dependencies?
- How does the presence of position embeddings influence a model's ability to capture long-range dependencies in a sequence?
- What is the relationship between the model's vocabulary size and its ability to reason over long-range dependencies in a sequence?
- Can you discuss the trade-offs between model capacity and computational efficiency when it comes to reasoning over long-range dependencies in a sequence?
- How do different initialization schemes for model weights affect the model's ability to capture long-range dependencies in a sequence?
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