Question 1

What problem do attention variants solve?

Accepted Answer

Standard attention is O(n^2) in sequence length; variants reduce this to O(n log n) or O(n), enabling longer context windows and faster inference.

Question 2

When should I use sparse attention vs. linear attention?

Accepted Answer

Sparse (local + strided) is better when relevant context is nearby; linear (Performer, Mamba) is better for very long sequences with global dependencies.

Question 3

Does Longformer sacrifice accuracy for speed?

Accepted Answer

Not significantly. Local windowed attention plus sparse global attention preserves important context. Trade-offs are task-dependent.

Question 4

What is FAVOR+ and why is it important?

Accepted Answer

FAVOR+ approximates softmax attention using random features; Performer uses it for linear-time attention without sacrificing accuracy. Elegant mathematical trick.

Question 5

How do I know which variant to use for my task?

Accepted Answer

Start with standard attention, measure memory/latency, then experiment. Long-document understanding → Longformer/BigBird; high-speed inference → Performer.

Question 6

Can variants replace standard attention entirely?

Accepted Answer

Not always. Some tasks (fine-grained attention requirements) still prefer O(n^2) standard attention. Hybrid approaches (local + sparse global) often win.

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