Something weird is happening with LLMs and chess

Maybe I'm really stupid... but perhaps if we want really intelligent models we need to stop tokenizing at all? We're literally limiting what a model can see and how it percieves the world by limiting the structure of the information streams that come into the model from the very beginning.

I know working with raw bits or bytes is slower, but it should be relatively cheap and easy to at least falsify this hypothesis that many huge issues might be due to tokenization problems but... yeah.

Surprised I don't see more research into radicaly different tokenization.

FWIW I think most of the "tokenization problems" are in fact reasoning problems being falsely blamed on a minor technical thing when the issue is much more profound.

E.g. I still see people claiming that LLMs are bad at basic counting because of tokenization, but the same LLM counts perfectly well if you use chain-of-thought prompting. So it can't be explained by tokenization! The problem is reasoning: the LLM needs a human to tell it that a counting problem can be accurately solved if they go step-by-step. Without this assistance the LLM is likely to simply guess.

I’m the one who will fight you including with peer reviewed papers indicating that it is in fact due to tokenization. I’m too tired but will edit this for later, so take this as my bookmark to remind me to respond.

I am aware of errors in computations that can be fixed by better tokenization (e.g. long addition works better tokenizing right-left rather than L-R). But I am talking about counting, and talking about counting words, not characters. I don’t think tokenization explains why LLMs tend to fail at this without CoT prompting. I really think the answer is computational complexity: counting is simply too hard for transformers unless you use CoT. https://arxiv.org/abs/2310.07923