Comment by aDyslecticCrow

In a very simplified view;

Those "tolkens" humans "count" are translated to a ~2048 (depends on model) floating point vector.

bird => {mamal, english, noun, Vertebrate, aviant} has one r but what if you make it 20% more "french". Is is still 1 r? That could be the word "bird" in french, or it could be a french speaking bird or a bird species common in france.

If nearest neibour distance to the vocabulary of every language makes the vector no longer map to "bird"; then the amount of rs' must change, using a series of trained conditional checks (with some efficiency where languages have some general spelling patterns).

That is such an unreasonable amount of compute, that it is likley faar cheaper, easier and more reliable to train the model to memorise the output:

{"MCP":"python", "content":"len((c for c in 'strawberry' if c='r'))"}

The attention mechanism allow LLMs to learn this kind of absurdly inefficient calculations. But we really shouldn't use LLMs where they're outperformed by trivial existing solutions.