Hello everyone, I have a dataset with news articles and real radio-messages written by journalists. Now I want to generate radio-messages that look like real radio-messages so that is must not be done manually anymore. I wanted to use a GAN structure that uses a CNN as Discriminator, and a LSTM as Generator (as literature from 2021 suggested). However, now that GPT has become very strong, I want to use GPT. Could I use GPT as both the Discriminator and the Generator, or only the Generator (using GPT as Generator seems to be good, but I will need to do prompt optimization). Has anyone got an opinion or suggestion (or paper/blog I could read into that I might have missed)? I am doing this for my thesis and it would help me out greatly. Or maybe I am too fixated in using a GAN structure, and you suggest me to look into something else.