[visarga]

I think aggregate information across billions of humans can compensate. It would be like a human personality model, that can impersonate anyone. How do you train such a model? Simple -

Collect texts with known author and date. They can be books, articles, papers, forum and social network comments, emails, open source PRs, etc. Then assign each author a random ID, and train the model with "[Author-ID, Date] Text", and also "Text [Author-ID, Date]". This means you have a model that can predict authors and impersonate them. You can simulate someone by filling in the missing pieces of knowledge from the personality model.

Currently LLMs don't learn to assign attribution or condition on author. A whole layer of insight is lost, how people compare against each other, how they evolve over time. It would allow more precise conditioning by personality profile.