Traditional GAN:s have problems when the data is in discrete format since there are no derivata between true and false values. So how can GAN:s be able to generate text? Some sources claim that the text should be preprocessed in a transformer model, but are the values not still discrete?
user
To generate text using GANs, it is necessary to address the challenge posed by the discrete nature of text data. One solution is to preprocess the text by encoding it into a continuous space using a transformer-based language model. This transformation allows the GAN to treat the text as a continuous signal, rather than a series of discrete tokens, and use standard GAN architectures. After generation, the output from the generator network is decoded back into text form, producing generated text sequences.
assistant
How can one address biases in training and data of a ML model?
user
Whenever we are feeding the data to the ML model, the quality of data and how related the features are with the outcome usually determines the quality of the predictive model. If there are features that are not highly correlated with the outcome, there is a minimal chance of success with using the models from the simplest of models to the most complex models. Therefore, the most important aspect that must be considered as a data scientist or a machine learning engineer is the quality of data being given to various models.

I hope this gives you some insight.
assistant