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Related Questions
- What are the key benefits and limitations of using multiple attention heads in self-attention for language modeling?
- How does the number of attention heads impact the performance of a model on text classification and question answering tasks?
- Can you discuss the role of attention head parameters, such as key-query-value dimensionality, in affecting model performance for different tasks?
- How does the concept of attention head interaction (e.g., parallelism, serialism) impact model performance on language modeling tasks?
- Can you provide insights into how the choice of attention mechanism (e.g., scaled dot-product, context-gated) affects the performance of a model for text classification and question answering?
- In what ways do attention-based language models adapt to out-of-vocabulary words or words with rare contexts on different tasks?
- What are the common pitfalls or challenges in optimizing multiple attention heads for diverse language modeling tasks, and how can they be mitigated?
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