Comment by AyyEye
5 months ago
With LLMs being unable to count how many Bs are in blueberry, they clearly don't have any world model whatsoever. That addition (something which only takes a few gates in digital logic) happens to be overfit into a few nodes on multi-billion node networks is hardly a surprise to anyone except the most religious of AI believers.
The core issue there isn't that the LLM isn't building internal models to represent its world, it's that its world is limited to tokens. Anything not represented in tokens, or token relationships, can't be modeled by the LLM, by definition.
It's like asking a blind person to count the number of colors on a car. They can give it a go and assume glass, tires, and metal are different colors as there is likely a correlation they can draw from feeling them or discussing them. That's the best they can do though as they can't actually perceive color.
In this case, the LLM can't see letters, so asking it to count them causes it to try and draw from some proxy of that information. If it doesn't have an accurate one, then bam, strawberry has two r's.
I think a good example of LLMs building models internally is this: https://rohinmanvi.github.io/GeoLLM/
LLMs are able to encode geospatial relationships because they can be represented by token relationships well. Teo countries that are close together will be talked about together much more often than two countries far from each other.
That is just not a solid argument. There are countless examples of LLMs splitting "blueberry" into "b l u e b e r r y", which would contain one token per letter. And then they still manage to get it wrong.
Your argument is based on a flawed assumption, that they can't see letters. If they didn't they wouldn't be able to spell the word out. But they do. And when they do get one token per letter, they still miscount.
> It's like asking a blind person to count the number of colors on a car.
I presume if I asked a blind person to count the colors on a car, they would reply “sorry, I am blind, so I can’t answer this question”.
> With LLMs being unable to count how many Bs are in blueberry, they clearly don't have any world model whatsoever.
Train your model on characters instead of on tokens, and this problem goes away. But I don't think this teaches us anything about world models more generally.
Actually I forgive them those issues that stem from tokenization. I used to make fun at them for listing datum as a noun whose plural form ends with an i, but once I learned about how tokenization works, I no longer do it - it feels like mocking a person's intelligence because of a speech impediment or something... I am very kind to these things, I think
Tokenization makes things harder, but it doesn't make them impossible. Just takes a bit more memorization.
Other writing systems come with "tokenization" built in making it still a live issue. Think of answering:
1. How many n's are in 日本?
2. How many ん's are in 日本?
(Answers are 2 and 1.)
> With LLMs being unable to count how many Bs are in blueberry, they clearly don't have any world model whatsoever.
Is this a real defect, or some historical thing?
I just asked GPT-5:
and it replied:
I also asked it how many Rs in Carrot, and how many Ps in Pineapple, amd it answered both questions correctly too.
It’s a historical thing that people still falsely claim is true, bizarrely without trying it on the latest models. As you found, leading LLMs don’t have a problem with it anymore.
The question is, did these LLMs figured it out by themselves or has someone programmed a specific coroutine to address this „issue“, to make it look smarter than it is?
On a trillion dollar budget, you could just crawl the web for AI tests people came up with and solve them manually. We know it‘s a massively curated game. With that kind of money you can do a lot of things. You could feed every human on earth countless blueberries for starters.
Calling an algorithm to count letters in a word isn’t exactly worth the hype tho is it?
The point is, we tend to find new ways these LLMs can’t figure out the most basic shit about the world. Horses can count. Counting is in everything. If you read every text ever written and still can’t grasp counting you simply are not that smart.
Depends how you define historical. If by historical you mean more than two days ago then, yeah, it's ancient history.
Some LLMs do better than others, but this still sometimes trips up even "frontier" non-reasoning models. People were showing this on this very forum with GPT-5 in the past couple days.
It is not historical:
https://kieranhealy.org/blog/archives/2025/08/07/blueberry-h...
Perhaps they have a hot fix that special cases HN complaints?
They clearly RLHF out the embarrassing cases and make cheating on benchmarks into a sport.
4 replies →
It was discussed and reproduced on GPT-5 on HN couple of days ago: https://news.ycombinator.com/item?id=44832908
Sibling poster is probably mistakenly thinking of the strawberry issue from 2024 on older LLM models.
Shouldn't the correct answer be that there is not a "B" in "blueberry"?
No, why?
It depend on context. English is often not very precise and relies on implied context clues. And that's good. It makes communication more efficient in general.
To spell it out: in this case I suspect you are talking about English letter case? Most people don't care about case when they ask these questions, especially in an informal question.
That was always a specious test.
LLMs don't ingest text a character at a time. The difficulty with analyzing individual letterings just reflected that they don't directly "see" letters in their tokenized input.
A direct comparison would be asking someone how many convex Bézier curves are in the spoken word "monopoly".
Or how many red pixels are in a visible icon.
We could work out answers to both. But they won't come to us one-shot or accurately, without specific practice.
> they clearly don't have any world model whatsoever
Then how did an LLM get gold on the mathematical Olympiad, where it certainly hadn’t seen the questions before? How on earth is that possible without a decent working model of mathematics? Sure, LLMs might make weird errors sometimes (nobody is denying that), but clearly the story is rather more complicated than you suggest.
> where it certainly hadn’t seen the questions before?
What are you basing this certainty on?
And even if you're right that the specific questions had not come up, it may still be that the questions from the math olympiad were rehashes of similar questions in other texts, or happened to correspond well to a composition of some other problems that were part of the training set, such that the LLM could 'pick up' on the similarity.
It's also possible that the LLM was specifically trained on similar problems, or may even have a dedicated sub-net or tool for it. Still impressive, but possibly not in a way that generalizes even to math like one might think based on the press releases.
> What are you basing this certainty on?
People make up new questions for each IMO.
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Like the other reply said, each exam has entirely new questions which are of course secret until the test is taken.
Sure, the questions were probably in a similar genre as existing questions or required similar techniques that could be found in solutions that are out there. So what? You still need some kind of world model of mathematics in which to understand the new problem and apply the different techniques to solve it.
Are you really claiming that SOTA LLMs don’t have any world model of mathematics at all? If so, can you tell us what sort of example would convince you otherwise? (Note that the ability to do novel mathematics research is setting the bar too high, because many capable mathematics majors never get to that point, and they clearly have a reasonable model of mathematics in their heads.)
I don’t solve math problems with my poetry writing skills:
https://chatgpt.com/share/689ba837-8ae0-8013-96d2-7484088f27...
Ask a kid that doesn't know how to read and write how many Bs there are in blueberry.
For a kid that doesn't know to read or write, Chat GPT writes way too much.