Attention mechanisms can only improve the performance of models when the input and output sequences are of similar lengths.

Attention mechanisms do not require input and output sequences to be of similar length; they work by letting each output position focus on relevant parts of the input regardless of size. The core idea is to compute a weighted sum of all input tokens for each output token, so even a long input can be summarized into a few key pieces. For example, a model translating a long paragraph into a short headline can still use attention to pick the most important sentences, even though the headline is much shorter. Thus, attention can improve performance in many cases where lengths differ, as it dynamically aligns information. The benefit comes from the ability to selectively weigh input tokens, not from matching lengths.