Resampling for imbalaced datasets: should testing set also be resampled? Apologies for what is probably a basic question but I have not been able to find a definitive answer either in the literature or in the Internet. 

When dealing with an imbalanced dataset one possible strategy is to resample either the minority or the majority class to artificially generate a balanced training set that can be used to train a machine learning model.

My doubt stems from the fact that a testing set is supposed to be a representation of what real-world data is going to look like. Under this assumption, my understanding is that the testing set is not to be resampled, unlike the training set, since the imbalance we are trying to deal with is present in the live data in the first place.

Could someone please clarify if this intuition is right?
You are right! As you said, the final goal of the model is to work with real life data distribution, therefor the test data has to stay unchanged.  

The goal of resampling is not to have the same number of objects from all classes, but to fit model to work with your data. So an intuitive example might be: if you have k objects of the first class and 5*k of the second, but your model learn data well, then resampling is not necessary. It's the reason, why we shouldn't use it during test (we have already trained model) 

One more thing that could be useful to remember is that F1 score could be calculate in multiclass task in different ways (it could account label imbalance), here is an example from sklearn https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html