Can someone (who know about LLMs) explain why the r's in strawberry thing is related to tokenization? I have no reason to believe an LLM would be better at counting letters if each was one token. It's not like they "see" any of it. Are they better at counting tokens than letters for some reason? Or is this just one of those things someone misinformed said to sound smart to even less informed people, that got picked up?
IME Reddit would scream "tokenization" at the strawberry meme until blue in the face, assuring themselves better tokenization meant the problem would be solved. Meanwhile RLHF'ers were/are en masse paid to solve the problem through correcting thousands of these "counting"/perfect syntax prompts and problems. To me, since RLHF work was being paid to tackle these problems, it couldn't be a simple tokenization problem. If there was a tokenization bottleneck that fixing would solve the problem, we would not be getting paid to so much money to RLHF synax-perfect prompts (think of Sudoku type games and heavy syntax-based problems).
No, why models are better are these problems now is because of RLHF. And before you say, well now models have learned how to count in general, I say we just need to widen the abstraction a tiny bit and the models will fail again. And this will be the story of LLMs forever--they will never take the lead on their own, and its not how humans process information, but it still can be useful.