> Any reason for this?

They're not trained for that. And there's no good reason to improve it if you can instead rerun the paragraph saying "make this slightly shorter".

> If a basic online word count tool can do this

It's an entirely different technology and not comparable at all. If you want to involve an actual word counter, this is not hard to integrate, with a basic loop that measures the output and feeds back the result so that the LLM can shorten/lengthen the text automatically before returning to you.

they don't see words, only tokens

and even with tokens they don't know how to count them at the LLM completion layer

they have to be trained with something like RLHF about word counting at the question answering / instruction following layers

or at the application layer (so called "agentic workflows"), e.g. writing a Python code to count words, or calling a function or a CLI tool like "wc"

The M stands for Model not Math

Same reason Pavlov's dog can't count either.

For LLMs, it's a meta-cognition task. Before they see anything, all text gets cut into pieces called tokens. Tokens contain letters, spaces, punctuation. LLMs never see the true punctuation or spaces, they only see these tokens. And by seeing these tokens, I mean the tokenizer simply says: I have a dictionary from text to tokens; I won't even show the token representation to you, just their position in the dictionary. For example, instead of showing "cat;", it just hands over entry #48712. The model has to deal with the rest.

So they'd need to do complex recall on resources of language structure it was trained on to be able to count accurately.

My picture over LLMs is this: I like to imagine what LLMs do is close to us trying to learn language from a dictionary of an alien language. We couldn't ground anything in reality, we maybe wouldn't know where words start or end in the definitions, but we can pattern match enough stuff to be useful for an alien giving us text queries.

I also asked GPT for a metaphor, and it came back with these:

- It’s like trying to clap to music and being asked, “Make it 100 words worth of claps.” You’re working with rhythm, not actual word units, so your sense of count is fuzzy.

- LLMs are excellent at flowing language but bad at rigid constraints — like a jazz musician who can improvise beautifully but can’t stop exactly on the 137th note without counting.