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Related Questions
- How does the attention mechanism in LLMs enable the model to capture long-range dependencies in sequential data?
- Can you explain the role of attention in mitigating the vanishing gradient problem and its impact on long-range dependencies?
- How do different attention mechanisms, such as self-attention and multi-head attention, affect the model's ability to capture long-range dependencies?
- What are some common challenges in capturing long-range dependencies in LLMs, and how can attention mechanisms be used to address them?
- Can you discuss the relationship between attention and the concept of 'position embeddings' in capturing long-range dependencies?
- How does the use of attention mechanisms in LLMs impact the model's ability to generalize to tasks that require long-range dependencies?
- Can you explain how attention mechanisms can be used to improve the model's ability to capture long-range dependencies in tasks such as machine translation and text summarization?
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