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Related Questions
- What is the key innovation in the transformer architecture that enables it to overcome the limitations of RNNs?
- How does the self-attention mechanism in transformers allow for parallelization, whereas RNNs are sequential?
- In what ways does the transformer's parallelization ability improve the efficiency of language model training?
- Can you provide an example of a real-world application where the transformer architecture has outperformed RNNs?
- What are the primary computational costs associated with training a transformer-based language model compared to an RNN-based model?
- How do transformers handle out-of-vocabulary words, and is this a limitation compared to RNNs?
- What are some potential future directions for research in transformer architecture and its applications in NLP?
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