The example I gave of multi-modal learning was really just highlighting a dichotomy in the techniques that we use in machine learning today. FWIW I am a couple of years removed from working heavily with tabular data, so do take this with a grain of salt. But there are essentially two different modeling approaches for two different types of datasets. On the one hand, you have deep learning (BERT, language models, CV models) which does well on raw data like text or images. These usually work by mapping the raw data to dense embeddings, which are the output of neural models. On the other hand, you have decision trees / forests (think XG boost) that work great on tabular data - spreadsheets or other data of that nature.
But what do you do if you have a spreadsheet of data and one of the columns is raw text data but the other columns are say sparse boolean features? How can you incorporation the extra information from the spreadsheet into your language model? I think this is a common problem in industry that there's not a clear solution for right now.