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GPT-5: "How many times does the letter b appear in blueberry?"

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Re: GPT-5: "How many times does the letter b appear in blueberry?"

#301

Post by visarga »

davidguetta wrote: post #292
visarga wrote: post #106

Let's change this game a bit. Spell "understanding" in your head in reverse order without spending twice more time than forward mode. Can you? I can't. Does that mean we don't really understand even simple spelling? It is a fun activity to dunk on LLMs, but let's have some perspective here.

I can do it if I write the word once and look at it, which is exactly what a transformer based llm is supposed to do.

It sees tokens not letters like us. And has to recite tokens in reverse order, and their letters in reverse order, over a set of 200K tokens. Token codes are arbitrary numbers associated with word fragments, they convey no letters.

Re: GPT-5: "How many times does the letter b appear in blueberry?"

#302

Post by skeledrew »

mdp2021 wrote: post #297
skeledrew wrote: post #245

Earlier quoted context omitted.

LLMs deliver pretty well on their intended functionality: they predict next tokens given a token history and patterns in their training data. If you want to describe that as fully intelligent, that's your call, but I personally wouldn't. And adding functionality that isn't directly related to improving token prediction is just bad practice in an already very complex creation. LLM tools exist for that reason: they're…

> given a token history and patterns in their training data. If you want to describe that as fully intelligent No, I would call (an easy interpretation of) that an implementation of unintelligence. Following patterns is what an hearsay machine does. The architecture you describe at the "token prediction" level collides with an architecture in which ideas get related with better justifications than frequent co-occurra…

> Following patterns is what an hearsay machine does.

That's also how the brain works, at least partially. Primary differences are it takes and processes (trains itself on) raw sensory data instead of character tokens, and it continually does so for every conscious moment from at least birth until death.

Re: GPT-5: "How many times does the letter b appear in blueberry?"

#303

Post by pxc »

handoflixue wrote: post #267
zahlman wrote: post #173

Earlier quoted context omitted.

I'll be impressed when you can reliably give them a random four-word phrase for this test. Because I don't think anyone is going to try to teach them all those facts; even if they're trained to know letter counts for every English word (as the other comment cites as a possibility), they'd then have to actually count and add, rather than presenting a known answer plus a rationalization that looks like counting and add…

5 "b"s, not counting the parenthetical at the end. https://claude.ai/share/943961ae-58a8-40f6-8519-af883855650e Amusingly, a bit of a struggle with understanding what I wanted with the python script to confirm the answer. I really don't get why people think this is some huge un-fixable blindspot...

I don't think the salience of this problem is that it's a supposedly unfixable blind spot. It's an illustrative failure in that it breaks the illusory intuition that something that can speak and write to us (sometimes very impressively!) also thinks like us.

Nobody who could give answers as good as ChatGPT often does would struggle so much with this task. The fact that an LLM works differently from a whole-ass human brain isn't actually surprising when we consider it intellectually, but that habit of always intuiting a mind behind language whenever we see language is subconscious and and reflexive. Examples of LLM failures which challenge that intuition naturally stand out.

Re: GPT-5: "How many times does the letter b appear in blueberry?"

#304

Post by sunaookami »

richard_cory wrote: post #123

This is consistently reproducible in completions API with `gpt-5-chat-latest` model: ``` curl ' https://api.openai.com/v1/chat/completions ' \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer ' \ --data '{ "model": "gpt-5-chat-latest", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "How many times does the letter b appear in blueberry" } ] } ], "temperature": 0, "max…

The "gpt-5-chat" model is a non-reasoning model and these struggle because of tokens.

Re: GPT-5: "How many times does the letter b appear in blueberry?"

#305

Post by melagonster »

Kim_Bruning wrote: post #260
windowshopping wrote: post #144

Earlier quoted context omitted.

What a terrible analogy. Illusions don't fool our intelligence, they fool our senses, and we use our intelligence to override our senses and see it for what it for it actually is - which is exactly why we find them interesting and have a word for them. Because they create a conflict between our intelligence and our senses. The machine's senses aren't being fooled. The machine doesn't have senses. Nor does it have int…

Agreed, it's not _biological_ intelligence. But that distinction feels like it risks backing into a kind of modern vitalism, doesn't it? The idea that there's some non-replicable 'spark' in the biology itself.

I worry about we do not even know how the brain or LLM works. And people directly declared that they are just same stuff.

Re: GPT-5: "How many times does the letter b appear in blueberry?"

#306

Post by jonrosner »

I can't reproduce this, even when forcing it not to reason:

"how many b's are in blueberry? don't think, answer fast."

blueberry • b → 1 (at position 1) • l → no • u → no • e → no • b → 2 (at position 5) • e → no • r → no • r → no • y → no

Total: 2 b’s.

Re: GPT-5: "How many times does the letter b appear in blueberry?"

#307

Post by vidarh »

mdp2021 wrote: post #295
vidarh wrote: post #254

Earlier quoted context omitted.

It is clear it is not, given we have examples of models that handles these cases. I don't even know what you mean with "architecturally all checks are implemented and mandated". It suggests you may think these models work very differently to how they actually work.

> given we have examples of models that handles The suggestions come from the failures, not from the success stories. > what you mean with "architecturally all checks are implemented and mandated" That NN-models have an explicit module which works as a conscious mind and does lucid ostensive reasoning ("pointing at things") reliably respected in their conclusion. That module must be stress-tested and proven as reliab…

> The suggestions come from the failures, not from the success stories.

That thinking is flawed. The successes conclusively proves that the issue isn't systemic because there is a solution.

> That NN-models have an explicit module which works as a conscious mind and does lucid ostensive reasoning ("pointing at things") reliably respected in their conclusion.

Well, this isn't how LLMs work.

> That module must be stress-tested and proven as reliable. Success stories only result based are not enough.

Humans aren't reliable. You're setting the bar at a level well beyond what is necessary, and almost certainly beyond what is possible.

> I am interested in how they should work.

We don't know how they should work, because we don't know what the optimal organisation is.

Re: GPT-5: "How many times does the letter b appear in blueberry?"

#308

Post by Minor49er »

I'm not sure if someone did this already, but I reproduced this in GPT-5 and asked it to explain how it got this wrong. It responded:

"Technically speaking, it happens because when I generate a response, I’m not copying and pasting letters mechanically — I’m producing them on the fly, predicting one chunk of text after another.

When I tried to highlight the b’s in “blueberry,” my output process went something like this:

1. Internally, I represented it as: b l u e b e b r r y

2. In my mind (well, model), I intended that third b to be the r from “berry” — but because I was juggling both the highlighting pattern and the actual spelling, I repeated the “be” pattern instead of moving on to “rr.”

3. Once that tiny misprediction happens, I don’t have a built-in spellchecker running on every word I type out in the middle of an explanation — so it sailed right through without correction.

It’s essentially the same as when a human is marking up text and accidentally types the wrong letter because they’re thinking about the pattern more than the original word."

Re: GPT-5: "How many times does the letter b appear in blueberry?"

#309

Post by taylorius »

philipwhiuk wrote: post #13
mikestorrent wrote: post #5

Earlier quoted context omitted.

This is a great idea. Like, if someone asked me to count the number of B's in your paragraph, I'd yeet it through `grep -o 'B' file.txt | wc -l` or similar, why would I sit there counting it by hand? As a human, if you give me a number on screen like 100000000, I can't be totally sure if that's 100 Million or 1 Billion without getting close and counting carefully. Should ought have my glasses. Mouse pointer helps som…

If you have to build an MCP for every system you aren’t building intelligence in the first place.

We have fingers and a keyboard / mouse, because that's the best thing we've come up with. If we could output binary to a bunch of program interfaces directly (perhaps via some neuralink type thing) we would surely take that option.

Re: GPT-5: "How many times does the letter b appear in blueberry?"

#310

Post by antonvs »

andrewmcwatters wrote: post #97
antonvs wrote: post #92

Earlier quoted context omitted.

> It's really disingenuous for the industry to call warming tokens for output, "reasoning," as if some autocomplete before more autocomplete is all we needed to solve the issue of consciousness. There's no obvious connection between reasoning and consciousness. It seems perfectly possible to have a model that can reason without being conscious. Also, dismissing what these models do as "autocomplete" is extremely disi…

It would require you to change the definition of reasoning, or it would require you to believe computers can think. A locally trained text-based foundation model is indistinguishable from autocompletion, and outputs very erratic text, and the further you train it's ability to diminish irrelevant tokens, or guide it to produce specifically formatted output, you've just moved its ability to curve fit specific requireme…

> It would require you to change the definition of reasoning

What matters here is a functional definition of reasoning: something that can be measured. A computer can reason if it can pass the same tests that humans can pass of reasoning ability. LLMs blew past that milestone quite a while back.

If you believe that "thinking" and "reasoning" have some sort of mystical aspect that's not captured by such tests, it's up to you to define that. But you'll quickly run into the limits of such claims, because if you want to attribute some non-functional properties to reasoning or thinking, that can't be measured, then you also can't prove that they exist. You quickly get into an intractable area of philosophy, which isn't really relevant to the question of what AI models can actually do, which is what matters.

> it does behave very much like a curve fitting search algorithm.

This is just silly. I can have an hours-long coding session with an LLM in which it exhibits a strong functional understanding of the codebase its working on, a strong grasp of the programming language and tools its working with, and writes hundreds or thousands of lines of working code.

Please plot the curve that it's fitting in a case like this.

If you really want to stick to this claim, then you also have to acknowledge that what humans do is also "behave very much like a curve fitting search algorithm." If you disagree, please explain the functional difference.

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