pooling

A transformer emits one vector per token, but retrieval needs one vector per text. The signal is knowing the pooling options and the catch that almost everyone misses: the model has to be trained for whichever one you pick.

CNNs are foundational even as transformers rise, and this checks whether you understand why convolution suits images. The signal is parameter sharing and local connectivity, what pooling buys, and how the receptive field grows. Here is the answer.