Analyze attention complexity and improvements

Q: Analyze attention complexity and improvements

This question evaluates understanding of Transformer self-attention in the Machine Learning domain, testing the ability to analyze time and space complexity, memory–computation trade-offs, and the role of approximation strategies for efficiency.

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In the context of Transformer-style models, analyze the computational complexity of self-attention.

Assume a sequence length of $n$ and hidden dimension $d$ .

Derive the time and space complexity of standard scaled dot-product self-attention.
Explain why this becomes a bottleneck for long sequences.
Describe at least three classes of methods that reduce the complexity (e.g., sparse attention, low-rank or kernel-based approximations, chunking/segmenting), including their high-level ideas and trade-offs.

Analyze attention complexity and improvements

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Analyze attention complexity and improvements

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