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Related Questions
- What are the key differences between attention-based and recurrent-based methods for handling long-range dependencies in LLMs?
- How do attention-based methods, such as self-attention, address the vanishing gradient problem in recurrent-based methods?
- Can you provide an example of how attention-based methods can be used to capture long-range dependencies in a sequence-to-sequence task?
- What are the computational and memory requirements of attention-based methods compared to recurrent-based methods?
- How do recurrent-based methods, such as LSTMs, handle long-range dependencies, and what are their limitations?
- Can you describe the trade-offs between attention-based and recurrent-based methods in terms of interpretability and explainability?
- How do attention-based methods, such as transformer architectures, handle out-of-vocabulary words and rare events in long-range dependencies?
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