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Related Questions
- What are the key differences between transformer-based and recurrent neural network (RNN) architectures in LLMs regarding memory and attention mechanisms?
- How do attention mechanisms help LLMs to selectively focus on relevant context when switching between different topics or tasks?
- Can you explain the concept of 'context window' in LLMs and how it impacts context switching?
- How do LLMs employ memory-augmented neural networks to manage long-term dependencies and context switching?
- What is the role of the 'memory' component in memory-augmented neural networks, and how does it facilitate context switching?
- How do LLMs use 'self-attention' mechanisms to weigh the importance of different context elements when switching between tasks?
- What are some common challenges in implementing memory and attention mechanisms in LLMs, and how can they be addressed?
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