Researchers are adapting NLP and large-language-model approaches such as masked autoencoders, but continue to debate loss functions and representations, partly because the data is often tabular.

“There's a lot of copying of what happens in NLP type of LLMs, right? And like masked autoencoder. So what's the typical way to go? But the type of losses, the type of representations, I think people are still debating maybe because you know, this is sort of often kind of tabular data.”