Small convolutional kernels such as 3x3 rather than a few large ones

This is very well explained in the VGGNet paper. There are 2 reasons: First, you can use several smaller kernels rather than few large ones to get the same receptive field and capture more spatial context, but with the smaller kernels you are using less parameters and computations.

Secondly, because with smaller kernels you will be using more filters, you'll be able to use more activation functions and thus have a more discriminative mapping function being learned by your CNN.