[NLG]: Does it make sense to add a discriminator model on top of pre-trained language generation models like GPT, XLNet, etc?

Background: I have a dataset of essays and some classes associated with them, the dataset is initially imbalanced, and collecting more data is not an option, so I was looking for Text Generation methods, I am trying to generate text samples that are almost identical to the original samples, so provided, I add keywords and classes as inputs to the generative model,


Is it necessary to add a discriminative model that predicts whether a sample is real or fake?

deeplearning generation gpt language language generation nlg sense top xlnet