Multilabel classification training data

Hi guys, just have a question regarding the training data for multilabel classification.

So, for multiclass classification, you can play around with the number of samples (K) per label, and of course, the higher the K, the performance increases; this is straightforward as there will be only 1 label per sample.

However, for multilabel classification, where there can be more than 1 label per sample (also in many different combinations), how are we supposed to construct our training data? for example, would it be best to give an equal number of samples for every combination of labels? this would exponentially increase the training data required which would defeat the purpose of few-shot learning...?

I am asking this question as I have tried training the model without considering the above question and getting not-so-great results (around 65% accuracy with 12 labels).

Thank you!