Does the attention mechanism increase computational complexity?

Yes, attention mechanisms do increase computational complexity relative to simpler sequence models like basic RNNs. The increase stems primarily from the pairwise comparisons needed to compute attention scores across all input tokens.

The key factor is the quadratic dependency on the input sequence length (O(n²) complexity), as each token's relationship to every other token is evaluated. This requires large matrix multiplications and scaling operations. While enabling superior context understanding, this significantly demands more computation and memory, especially for long sequences. Alternatives like linear attention or sparse attention aim to mitigate this cost.

Despite the increased complexity, attention's ability to model long-range dependencies effectively makes it highly valuable. Careful implementation (like optimized libraries), hardware acceleration (GPUs/TPUs), and algorithmic optimizations are crucial to manage the computational burden and maintain feasibility for large-scale applications like machine translation and text generation. The performance gains often justify the cost.