Attention can be improved by treating it like gradient boosting: a second attention pass with separate projections learns to correct the first pass's mistakes, boosting performance without major architectural changes.
This paper improves transformer attention by adding a second pass that corrects the first pass's errors, similar to how gradient boosting works in machine learning. The method uses a gated correction mechanism and achieves better language modeling performance than standard attention with minimal computational overhead.