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Related Questions
- What are the key differences between the transformer architecture and traditional recurrent neural networks in language modeling?
- How does the self-attention mechanism in the transformer architecture enable parallelization and improve training efficiency?
- Can you explain the role of positional encoding in the transformer architecture and its impact on model performance?
- How does the use of multi-head attention in the transformer architecture improve model performance compared to single-head attention?
- What are the benefits of using the transformer architecture for language modeling tasks such as machine translation and text summarization?
- How does the transformer architecture handle out-of-vocabulary words and unknown tokens during training and inference?
- Can you compare the performance of transformer-based language models with traditional language models such as LSTM and GRU on various benchmark datasets?
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