GANs (Generative Adversarial Networks) can generate text by transforming the text into a continuous representation, such as a multidimensional vector. This continuous representation can then be used as input for the generator and discriminator in the GAN. The preprocessing of the text can be done using a transformer model or other techniques that map text to a continuous representation. Note that the output generated by the GAN must also be transformed back from the continuous representation into text form.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence