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Related Questions
- What are the key differences between self-attention and traditional recurrent neural network (RNN) architectures in large language models?
- How does self-attention enable large language models to capture long-range dependencies and contextual relationships in input sequences?
- Can you explain the concept of 'attention' in the context of large language models and how it relates to contextual retention?
- What are some common applications of self-attention in large language models, such as machine translation, text summarization, and question answering?
- How does the use of self-attention in large language models impact their ability to generalize to out-of-domain data?
- What are some potential limitations or challenges associated with using self-attention in large language models, such as computational efficiency or interpretability?
- Can you discuss the relationship between self-attention and other contextual retention methods, such as transformer layers and hierarchical attention mechanisms?
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