5 ms·

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 fe

by AyyEye 1y 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.

yosefk 1y ago
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
- astrange 1y ago
  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.)
andyjohnson0 1y ago
> 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: How many "B"s in "blueberry"? and it replied: There are 2 — the letter b appears twice in "blueberry". I also asked it how many Rs in Carrot, and how many Ps in Pineapple, amd it answered both questions correctly too.
- libraryofbabel 1y ago
  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.
  - pydry 1y ago
    Depends how you define historical. If by historical you mean more than two days ago then, yeah, it's ancient history.
  - jijijijij 1y ago
    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.
  - pxc 1y ago
    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.
- ThrowawayR2 1y ago
  It was discussed and reproduced on GPT-5 on HN couple of days ago: https://news.ycombinator.com/item?id=44832908 https://news.ycombinator.com/item?id=44832908 Sibling poster is probably mistakenly thinking of the strawberry issue from 2024 on older LLM models.
- bgwalter 1y ago
  It is not historical: https://kieranhealy.org/blog/archives/2025/08/07/blueberry-hill/ https://kieranhealy.org/blog/archives/2025/08/07/blueberry-h... Perhaps they have a hot fix that special cases HN complaints?
  - AyyEye 1y ago
    They clearly RLHF out the embarrassing cases and make cheating on benchmarks into a sport.
    - Terr_ 1y ago
      I wouldn't be surprised if some models get set up to identify that type of question and run the word through string processing function.
      - jijijijij 1y ago
        Of course they do stuff like that, otherwise it would look like they are stagnating. Fake it till you make it. Tho, at this point, the world is in deep shit, if they don’t make it…
        pmg101 1y ago
        What deep shit do you foresee? My prediction is that this will be like the 2000 dot com bubble. Both dot com and AI are real and really useful technologies but hype and share price has got way ahead of it so will need to re adjust.
        jijijijij 1y ago
        A major economic crisis, yes. I think the web is already kinda broken because of AI, gonna get a lot worse. I also question its usefulness… Is it useful solving any real problems, and if so how long before we run out of these problems? Because we conflated a lot of bullshit with innovation right before AI. Right now people may be getting a slight edge, but it’s like getting a dishwasher, once expectations adjusted things will feel like a grind again, and I really don’t think people will like that new reality in regard to experience of self-efficacy (which is important for mental health). I presume the struggle to get information, figuring it out yourself, may be a really important part of putting pressure towards process optimization and for learning, cognitive development. We may collectively regress there. With so many major crisis, a potential economic crisis on top, I am not sure we can afford losing problem solving capabilities to any extent. And I really, really don’t think AI is worth the fantastical energy expenditure, waste of resources and human exploitation, so far.
- nosioptar 1y ago
  Shouldn't the correct answer be that there is not a "B" in "blueberry"?
  - eru 1y ago
    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.
BobbyJo 1y ago
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/ 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.
- vrighter 1y ago
  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.
- xigoi 1y ago
  > 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”.
libraryofbabel 1y ago
> 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.
- simiones 1y ago
  > 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.
  - eru 1y ago
    > What are you basing this certainty on? People make up new questions for each IMO.
    - fxtentacle 1y ago
      Didn’t OpenAI get caught bribing their way to pre-tournament access of the questions?
      - eru 1y ago
        This is the first time I hear about this. (It's certainly possible, but I'd need to see some evidence or at least a write-up.) OpenAI got flamed over announcing their results before the embargo was up: IMO had asked companies to wait at least a week or so after the human winners were announced to announce the AI results. OpenAI did not wait.
williamcotton 1y ago
I don’t solve math problems with my poetry writing skills: https://chatgpt.com/share/689ba837-8ae0-8013-96d2-7484088f2774 https://chatgpt.com/share/689ba837-8ae0-8013-96d2-7484088f27...
eru 1y ago
> 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.
derdi 1y ago
Ask a kid that doesn't know how to read and write how many Bs there are in blueberry.
- Ygg2 1y ago
  For a kid that doesn't know to read or write, Chat GPT writes way too much.
Nevermark 1y ago
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.

by AyyEye 1y ago

yosefk 1y ago

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

astrange 1y ago
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.)

andyjohnson0 1y ago

> 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: How many "B"s in "blueberry"? and it replied: There are 2 — the letter b appears twice in "blueberry". I also asked it how many Rs in Carrot, and how many Ps in Pineapple, amd it answered both questions correctly too.

libraryofbabel 1y ago
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.
- pydry 1y ago
  Depends how you define historical. If by historical you mean more than two days ago then, yeah, it's ancient history.
- jijijijij 1y ago
  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.
- pxc 1y ago
  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.
ThrowawayR2 1y ago
It was discussed and reproduced on GPT-5 on HN couple of days ago: https://news.ycombinator.com/item?id=44832908 https://news.ycombinator.com/item?id=44832908 Sibling poster is probably mistakenly thinking of the strawberry issue from 2024 on older LLM models.
bgwalter 1y ago
It is not historical: https://kieranhealy.org/blog/archives/2025/08/07/blueberry-hill/ https://kieranhealy.org/blog/archives/2025/08/07/blueberry-h... Perhaps they have a hot fix that special cases HN complaints?
- AyyEye 1y ago
  They clearly RLHF out the embarrassing cases and make cheating on benchmarks into a sport.
  - Terr_ 1y ago
    I wouldn't be surprised if some models get set up to identify that type of question and run the word through string processing function.
    - jijijijij 1y ago
      Of course they do stuff like that, otherwise it would look like they are stagnating. Fake it till you make it. Tho, at this point, the world is in deep shit, if they don’t make it…
      - pmg101 1y ago
        What deep shit do you foresee? My prediction is that this will be like the 2000 dot com bubble. Both dot com and AI are real and really useful technologies but hype and share price has got way ahead of it so will need to re adjust.
        jijijijij 1y ago
        A major economic crisis, yes. I think the web is already kinda broken because of AI, gonna get a lot worse. I also question its usefulness… Is it useful solving any real problems, and if so how long before we run out of these problems? Because we conflated a lot of bullshit with innovation right before AI. Right now people may be getting a slight edge, but it’s like getting a dishwasher, once expectations adjusted things will feel like a grind again, and I really don’t think people will like that new reality in regard to experience of self-efficacy (which is important for mental health). I presume the struggle to get information, figuring it out yourself, may be a really important part of putting pressure towards process optimization and for learning, cognitive development. We may collectively regress there. With so many major crisis, a potential economic crisis on top, I am not sure we can afford losing problem solving capabilities to any extent. And I really, really don’t think AI is worth the fantastical energy expenditure, waste of resources and human exploitation, so far.
nosioptar 1y ago
Shouldn't the correct answer be that there is not a "B" in "blueberry"?
- eru 1y ago
  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.

BobbyJo 1y ago

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/ 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.

vrighter 1y ago
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.
xigoi 1y ago
> 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”.

libraryofbabel 1y ago

> 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.

simiones 1y ago
> 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.
- eru 1y ago
  > What are you basing this certainty on? People make up new questions for each IMO.
  - fxtentacle 1y ago
    Didn’t OpenAI get caught bribing their way to pre-tournament access of the questions?
    - eru 1y ago
      This is the first time I hear about this. (It's certainly possible, but I'd need to see some evidence or at least a write-up.) OpenAI got flamed over announcing their results before the embargo was up: IMO had asked companies to wait at least a week or so after the human winners were announced to announce the AI results. OpenAI did not wait.

williamcotton 1y ago

I don’t solve math problems with my poetry writing skills: https://chatgpt.com/share/689ba837-8ae0-8013-96d2-7484088f2774 https://chatgpt.com/share/689ba837-8ae0-8013-96d2-7484088f27...

eru 1y ago

> 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.

derdi 1y ago

Ask a kid that doesn't know how to read and write how many Bs there are in blueberry.

Ygg2 1y ago
For a kid that doesn't know to read or write, Chat GPT writes way too much.

Nevermark 1y ago

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.