Is there any effort made to compress text (and maybe other media) using prediction of next word and thus sending only the order number of the word/token which will be predicted on the client side
Server text: This is an example of a long text example, custom word flerfom inserted to confuse, that may appear on somewhere
Compressed Text transmitted : This [choice no 3] [choice no 4] [choice no 1] [choice no 6] [choice no 1] [choice no 3] [choice no 1], custom word flerfom [choice no 4] inserted [choice no 4] confuse [choice no 5] [choice no 4] [choice no 6] [choice no 5] on somewhere
(Note: of course [choice no 3] will be shortened to  to save bytes and also maybe we can do much better in some cases by sending the first letter of the word)
of course it means that the client side neural network has to be static or only updated in a predictable fasion, so the server knows for sure that the client neural network's predictions will follow the given choice orders. I tried example with https://demo.allennlp.org/next-token-lm, but the prediction is not that good. maybe gpt-3 can do better . but its too heavy for use in a normal pc / mobile device
In more details, the process is
Deploy the same model on both sides
Predict the next word after the starting word
Keep the prediction limit say 100
For any word which have more than 2 characters we do the prediction
If the current word is predicted within the top 100 predictions of the model , we can essentially replace it with a numeric char between 0-99 (inclusive) so we are replacing a say , 5 character word with a 2 character numerical char..
And if the word is not predicted in top 100 we send the word as it is..
As much better the model predicts, that much better the compression
And under no scenario it will work worse than the existing method..