27-12-2025, 04:24 PM
Hi,
I assume you mean something like "fine-tune" when you say "train", I don't think it's possible to train an LLM based on the text of the manuscript only, it would take a few orders of magnitude more data.
I think it should be possible to create some prediction model and check how much the actual text outperforms randomly shuffled tokens. I don't know if the result will have any practical application. Suppose, the result is 4.1635 as in your experiment, what are the conclusions here? After all, natural text will show some degree of unpredictability and at the same time random sequences generated using available in the XV century methods will show some degree of predictability.
Checking if it's easier to predict a token based on the left side context or the right side context looks interesting, but again I'm not sure about the implications. I don't even know what would happen if we run this on a normal text. I guess both values would be roughly the same?
I assume you mean something like "fine-tune" when you say "train", I don't think it's possible to train an LLM based on the text of the manuscript only, it would take a few orders of magnitude more data.
I think it should be possible to create some prediction model and check how much the actual text outperforms randomly shuffled tokens. I don't know if the result will have any practical application. Suppose, the result is 4.1635 as in your experiment, what are the conclusions here? After all, natural text will show some degree of unpredictability and at the same time random sequences generated using available in the XV century methods will show some degree of predictability.
Checking if it's easier to predict a token based on the left side context or the right side context looks interesting, but again I'm not sure about the implications. I don't even know what would happen if we run this on a normal text. I guess both values would be roughly the same?