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Can modern LLMs count the number of b's in "blueberry"?
- Mindless2112 1y agoIt seems like this could be easily solved in models that support tool calling by providing them with a tool that takes a token and returns the individual graphemes. It doesn't seem valuable for the model to memorize the graphemes in each of its tokens.
- jandrese 1y agoYes, but are you going to special case all of these pain points? The whole point of these LLMs is that they learn from training data, not from people coding logic directly. If you do this people will come up with a dozen new ways in which the models fail. They are really not hard to find. Basically asking them to do anything novel is at risk of complete failure. The interesting bit is that LLMs tend to work best a "medium difficulty" problems. Homework questions and implementing documented APIs and things like that. Asking them to do anything completely novel tends to fail as does asking them to do something so trivial that normal humans won't bother even writing it down.
- BobbyJo 1y agoIt makes sense when users ask for information not available in the tokenized values though. In the abstract, a tool that changes tokenization for certain context contents when a prompt references said contents is probably necessary to solve this issue (if you consider it worth solving).
- jandrese 1y agoIt's a fools errand. The kinds of problems you end up coding for are the ones that are blatantly obvious and ultimately useless except as a gotcha to the AI engines. All you're doing is papering over the deficiency of the model without actually solving a problem.
- BobbyJo 1y agoThis is less a deficiency of the model, and more of a deficiency of the encoder IMO. You can consider the encoder part of the model, but I think the semantics of our conversation require differentiating between the two.
- Mindless2112 1y agoTokenization is an inherent weakness of current LLM design, so it makes sense to compensate for it. Hopefully some day tokenization will no longer be necessary.
- poemxo 1y agoThat takes away from the notion that LLMs have emergent intelligent abilities. Right now it doesn't seem valuable for a model to count letters, even though it is a very basic measure of understanding. Will this continue in other domains? Will we be doing tool-calling for every task that's not just summarizing text?
- strbean 1y agoHow is counting letters a measure of understanding, rather than a rote process? The reason LLMs struggle with this is because they literally aren't thinking in English. Their input is tokenized before it comes to them. It's like asking a Chinese speaker "How many Rs are there in the word 草莓".
- poemxo 1y agoIt shows understanding that words are made up of letters and that they can be counted Since tokens are atomic, which I didn't realize earlier, then maybe it's still intelligent if it can realize it can extract the result by writing len([b for b in word if b == my_letter]) and decide on its own to return that value.
- xigoi 1y agoBut why doesn’t the LLM reply “I can’t solve this task because I see text as tokens”, rather than give a wrong answer?
- mjr00 1y ago> Will we be doing tool-calling for every task that's not just summarizing text? spoiler: Yes. This has already become standard for production use cases where the LLM is an external-facing interface; you use an LLM to translate the user's human-language request to a machine-ready, well-defined schema (i.e. a protobuf RPC), do the bulk of the actual work with actual, deterministic code, then (optionally) use an LLM to generate a text result to display to the user. The LLM only acts as a user interface layer.
- strbean 1y agoWe're up to a gazillion parameters already, maybe the next step is to just ditch the tokenization step and let the LLMs encode the tokenization process internally?
- bachittle 1y agoOpenAI definitely tarnished the name of GPT-5 by allowing these issues to occur. It's clearly a smaller model optimized for cost and speed. Compare it to GPT-4.5 which didn't have these errors but was "too expensive for them". This is why Anthropic naming system of haiku sonnet and opus to represent size is really nice. It prevents this confusion.
- NoahZuniga 1y ago> This is why Anthropic naming system of haiku sonnet and opus to represent size is really nice. It prevents this confusion. In contrast to GPT-5, GPT-5 mini and GPT-5 nano?
- Taek 1y agoThe problem is that GPT-5 is a smaller model than its predecessors.
- csallen 1y agoBut there's nothing in Claude's naming scheme stopping Claude 5 from being smaller than its predecessors.
- prophesi 1y agoI think it's a valid complaint that the naming scheme for the various GPT-4 models were very confusing. GPT-5 just launched, and doesn't (yet?) have a GPT-5 Turbo or GPT-o5 mini to muddy the waters.
- tempodox 1y agoIn marketing, confusion is a feature, not a bug.
- rootnod3 1y agoI mean yeah, but to many non-native speakers, sonnet and opus don't immediately convey size or complexity of the models.
- parpfish 1y agoProject manager: “great news! Our model can count Rs in strawberry!” Dev: “What about Bs in blueberry?” PM: “you’ll need to open a new jira ticket”
- bwfan123 1y agothis is literally what likely happens at these companies. ie, have teams that monitor twitter/social media for fails, and fix them with data patches.
- bigstrat2003 1y agoWhich is why I don't trust any of the benchmarks LLM enthusiasts point to when they say "see the model is getting better". I have zero confidence that the AI companies are trying to make the system better, rather than using the measure as a target.
- SpaceNoodled 1y agoThat reminds me of the time I found thread-safety-breaking changes in Intel's custom Android framework that were clearly designed to cheat benchmarks.
- rwmj 1y agoCan modern humans count the number of tokens in "blueberry"? (You're not allowed to cheat and use https://platform.openai.com/tokenizer https://platform.openai.com/tokenizer)
- ryanmcbride 1y agoI see this argument every time this particular failing is brought up and like, regardless of the reasoning, it's still completely failing to do something a second grader should be able to do and until it stops failing in that way, being like "Um technically it's a lot harder than you think" isn't going to win over AI skeptics.
- strbean 1y agoA Chinese second grader would probably not be able to answer "How many Rs are in the word 草莓?" It's a valid critique.
- ryanmcbride 1y agoYou're right they probably wouldn't be able to to it, but that child isn't being marketed as an invaluable business tool to assist doctors in diagnosing cancer, or whatever.
- strbean 1y agoSure. A non-English speaking adult wouldn't be able to answer the question either, after the question was translated to their language. Maybe you wouldn't want a non-English speaker helping you to write an acrostic in English. Luckily nobody is marketing LLMs as "great for designing word puzzles" though.
- ryanmcbride 1y agoYou can make comparisons all you want but you can't change the fact that it just plain looks bad.
- crtasm 1y ago3 days ago: https://news.ycombinator.com/item?id=44832908 https://news.ycombinator.com/item?id=44832908
- dang 1y agoThanks! Macroexpanded: GPT-5: "How many times does the letter b appear in blueberry?" - https://news.ycombinator.com/item?id=44832908 https://news.ycombinator.com/item?id=44832908 - Aug 2025 (321 comments)
- Kwpolska 1y agoThe Internet has been poking fun at LLMs failing to count letters for many months. Are the AI companies really living in an echo chamber? They should have implemented a thing to count letters (and to search for palindromes, and…), and just have the LLM pass the request to the count_letters function, as they do for many other tasks already…
- lgl 1y agoSo... The LLM only goes into effect after 10000 "old school" if statements?
- shakow 1y agoExpert systems with extra steps!
- andrewla 1y agoWhy on earth would they do this? This is not a fundamentally useful task; it serves as a measure of the LLM's ability to generalize to tasks outside of its training data and that strain the limits of what it can express.
- mingus88 1y agoBecause optics matter. they are all ultimately fundraising and competing and this is terrible PR Ask Jeeves from 1997 could answer this question, so tell me why we need to devote a nation-state amount of compute power to feed an “AI” that confidently gets kindergarten level questions dead ass wrong? I have the same kind of question when I watch the AI summary on Google output tokens one-by-one to give me less useful information that is right there on the first search result from Wikipedia (fully sourced, too)
- mcphage 1y agoIf you’re advertising that your new LLM is like a PhD in your pocket, and it fails on a task that a first grader can do, it makes it hard to take your other claims seriously.
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- Zenst 1y agoI gave it a puzzle recently to test it out "An Ex-cop lost has home, his car and his girlfriend. What did he loose first?" The AI thought and concluded that he had lost his job first, until I pointed out that it was not the first thing he had lost - which was his umbilical cord, a far better answer, in the AI's opinion. Which raises many aspects - Can an AI disagree with you? Will AI develop solid out-of-the-box thinking as well as in-the-box thinking, will it grasp applying both for a thru the box thinking and solutions... After all, we have yet to perfect the teaching of children, so the training of AI, has a long way to go and will get down to quality over quantity, just deciding what is quality and what is not. After all - Garbage in, Garbage out, is probably more important today than it ever was in the history of technology.
- wslh 1y agoBeyond the hype, and even the recent breakthroughs in LLMs, it would be very valuable to start compiling and summarizing their specific limitations.
- ninetyninenine 1y ago>This is because the transformers are not able to take in or output actual text efficiently. Instead, the text is converted into numerical representations of itself, which is then contextualized to help the AI come up with a logical response. In other words, the AI might know that the tokens “straw” and “berry” make up “strawberry,” but it may not understand that “strawberry” is composed of the letters “s,” “t,” “r,” “a,” “w,” “b,” “e,” “r,” “r,” and “y,” in that specific order. Thus, it cannot tell you how many letters — let alone how many “r”s — appear in the word “strawberry.” This is a great example. The LLM doesn't know something but it makes up something in it's place. Just because it made up something doesn't mean it's incapable of reasoning. The thing with LLMs is that they can reason. There's evidence for that. But they can also be creative. And the line between reasoning and creativity at a low level is a bit of a blur as reasoning is a form of inference but so is creativity. So when an LLM reasons or gets creative or hallucinates it's ultimately doing the same type of thing: inference. For us, we have mechanisms in our brain that allow us to tell the difference most of the time. The LLM does not. That's the fundamental line. And I feel because of this we are literally really close to AGI. A lot of people argue the opposite. They think reasoning and is core to intelligence and a separate concept from creativity and that all LLMs lack reasoning. I disagree. In fact humans ourselves have trouble separating hallucination from reasoning. Look at religion. Religion permeates our culture but it's basically all hallucinations that we ultimately mistake for reasoning. Right? Ask any christian or muslim, the religions make rational sense to them! They can't tell the difference. So the key is to give the LLM the ability to know the difference. Is there some way to build into the transformer, some way to quantify whether something is fact or fiction? Like let's say the answer to a prompt created an inferenced datapoint that's very far far away from a cluster of data. From that we can derive some metric that quantifies how likely the response is based on evidence? Right? The whole thing is on a big mathematical multidimensional durve. If the inferenced point on the curve is right next to existing data then it must be more likely to be true. If it's far away in some nether region of the curve then it's more likely to be false. If the LLM can be more self aware and we can build this quantitative metric into the network then use reinforcement learning to kind of have the network be less sure about an answer if it's far away from a cluster of training data points we can likely very much improve the hallucination problem. Of course I'm sure this is a blunt instrument as even false inferences data can be very close to existing training data. But at least this gives the LLM some level of self awareness of how reliable it's own answer was.
- mullingitover 1y agoThe hilarious thing is that LLMs will happily explain the token problem to you, and will even list out exactly which words where will have these problems. Then in the same breath when you ask it about letter counts, it will screw up.
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- jerf 1y agoIt might help a bit to expand this test to a short phrase. With such a small test the model can be right for the wrong reasons; opening up a bit of space to be wrong in might sharpen the differences. (My one-off test of the default ChatGPT model, whatever that is, got 'How many b's are there in "Billy Bob beat the record for bounding?"' correct first try, with correct reasoning given.)
- fantasizr 1y agoperhaps apocryphal but “A single lie discovered is enough to create doubt in every truth expressed.”. Why trust these tools for the hard things when we don't for the 'easy' ones.
- dvrj101 1y agoi tried with three different models : - hf.co/lmstudio-community/DeepSeek-R1-Distill-Llama-8B-GGUF:Q8_0 - qwen3:4b-instruct-2507-fp16 - hf.co/ibm-granite/granite-3.3-2b-instruct-GGUF:F16 ollama run qwen3:4b-instruct-2507-fp16 >>> how many b’s are there in blueberry? Let's break down the word *"blueberry"* step by step to count how many *b's* are in it. Step 1: Write out the word clearly: *b l u e b e r r y* Step 2: Go through each letter one by one: - b → yes, a *b* - l → no - u → no - e → no - b → yes, another *b* - e → no - r → no - r → no - y → no Step 3: Count the *b's*: - First *b* at the beginning - Second *b* after "blue" So, there are *two b's*. Final answer: *2* b's in "blueberry". >>> ollama run hf.co/ibm-granite/granite-3.3-2b-instruct-GGUF:F16 >>> how many b’s are there in blueberry? The word "blueberry" contains two 'b's. (fastest lol, granite models are pretty underated) r1-distill output was similar to qwen instruct one but it double checked it's thinking part
- dvrj101 1y agoadded screenshots: https://files.catbox.moe/6q0v4t.png https://files.catbox.moe/6q0v4t.png https://files.catbox.moe/5t32fl.png https://files.catbox.moe/5t32fl.png
- ChrisArchitect 1y agoRelated: GPT-5: "How many times does the letter b appear in blueberry?" https://news.ycombinator.com/item?id=44832908 https://news.ycombinator.com/item?id=44832908
- tocs3 1y agoA while back I encoded the message "Can you read this?" in rot13 (or something very similar) and sent it to chatGPT[. It answered back (yes) in with a sentence in rot13. Why could it do that but not count letters?
- AaronAPU 1y agoAt the VERY beginning I asked it to reverse a hash, and was stunned that it was able to. This was in that first moment when the transformer burst on the scene. Turned out it just knew that particular string because it was on the internet. I don’t recall exactly but it was probably “hello world” or something like that. Anyway to your specific question, many of the models call Python tools behind the scenes now.
- qafy 1y agotldr; yes, except for GPT-5