I feel the need to clarify this. Teacher forcing is something very specific in such models, not simply the task of “producing the target sentence given source text”. I think that what you mean is correct, but it is written a bit ambiguously.
In teacher forcing in an autoregressive decoder you give the decoder the correct previous output tokens. So for instance, in machine translation during decoding, you would “guide” it by giving it the correct previous token for the prediction of each next token.
I am not sure about the models that you list, but in MT implementations it is common to randomly apply teacher forcing for different batches to ensure that the model generalizes better and is less prone to exposure bias.