When considering the Transformer model, very long sequences can be dealt with the
a) Longformer variant
b) BigBird variant
c) Star Transformers variant
d) Hierarchical variant
e) None of the above
Answer (2)
The Longformer variant of the Transformer model is specifically designed to handle very long sequences efficiently through a sparse attention mechanism. Other variants like BigBird also address this issue, but Longformer is the most recognized for this purpose. Thus, the chosen answer is option a) Longformer variant.
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When considering the Transformer model, dealing with very long sequences is a challenge because of the model's quadratic complexity with respect to sequence length. Some variations have been developed to address this issue by making them more efficient for longer sequences. These include:
Longformer Variant : Optimized for long documents by using a combination of original local self-attention and a dilated attention mechanism, which expands attention spans and reduces computational costs.
BigBird Variant : Introduces global tokens and sparse attention patterns to significantly reduce the quadratic complexity of traditional Transformers, making it suitable for long sequences while maintaining a good performance.
Star Transformers Variant : Uses a star-shaped attention mechanism that connects all tokens to a central relay node, allowing efficient information flow with reduced complexity.
Given these options, the Longformer, BigBird, and Star Transformers variants are all designed to handle longer sequences efficiently. Therefore, the correct choices are (a) Longformer variant , (b) BigBird variant , and (c) Star Transformers variant . The option 'd) Hierarchical variant' does not specifically refer to a common transformer variant for long sequences, nor does 'e) None of the above'.
From this detailed information, the correct choices include options (a), (b), and (c) focusing on handling long sequences within the Transformer model framework.
