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Related Questions
- How do attention weights impact the performance of transformer-based language models in tasks such as machine translation and text summarization?
- Can you explain the difference between self-attention and multi-head attention in the context of transformer architecture?
- What is the role of attention weights in determining the importance of different input tokens in a sentence during the encoding process?
- How do attention weights affect the interpretability of transformer-based language models, and are there any techniques to visualize them?
- Are there any limitations or challenges associated with using attention weights in transformer-based language models, and how are they being addressed?
- Can you provide an example of how attention weights can be used to improve the performance of a language model in a specific task, such as sentiment analysis?
- How do attention weights interact with other components of the transformer architecture, such as the encoder and decoder, to produce the final output?
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