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Related Questions
- What were the primary challenges in traditional recurrent neural network (RNN) architectures that led to the development of transformer-based models?
- How did the introduction of self-attention mechanisms in the transformer architecture improve the handling of long-range dependencies in NLP tasks?
- What was the role of parallel processing in the development of transformer-based architectures, and how did it contribute to their efficiency?
- How did the transformer architecture's ability to model context and relationships between words improve the performance of NLP tasks such as machine translation and text summarization?
- What were some of the key differences between the transformer architecture and other popular NLP architectures, such as RNNs and Long Short-Term Memory (LSTM) networks?
- How has the transformer architecture been applied to various NLP tasks, including question answering, sentiment analysis, and text classification?
- What are some potential limitations and challenges of transformer-based architectures, and how are researchers addressing these issues?
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