It’s a bit of a weird shower thought but basically I was wondering hypothetical if it would be possible to take data from a social media site like Reddit and map the most commonly used words starting at 1 and use a separate application to translate it back and forth.
So if the word “because” was number 100 it would store the value with three characters instead of seven.
There could also be additions for suffixes so “gardening” could be 5000+1 or a word like “hoped” could be 2000-2 because the “e” is already present.
Would this result in any kind of space savings if you were using larger amounts of text like a book series?
That’s pretty much what a tokenizer does for Large Language Models like Chat-GPT. You can see how it works here: https://platform.openai.com/tokenizer
Type in the word ‘Antidisestablishmentarianism’ and you can see it becomes 5 tokens instead of 28 characters.