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I tried my own test recently:

"Write a history of the Greek language but reverse it, so that one would need to read it from right to left and bottom to top."

ChatGPT wrote the history and showed absolutely no awareness, let alone, "understanding" of the second half of the prompt.

With o3-mini-high (just the last paragraph):

civilization Mycenaean the of practices religious and economic, administrative the into insights invaluable provides and B Linear as known script the in recorded was language Greek the of form attested earliest The

Oh, interesting, what do you get when you specify that the letters need to be reversed, too? (That was what I meant and the original prompt explicitly stated that requirement. I forgot to include it in the summary of my 'test' here.)

As much I think AI is overhyped too, that is a prime use case that would be better solved by passing the text to a tool, rather than jam a complex transformations like that into its latent space.

A real intelligence would recognize that this task is better solved with an automated tool and actually do so. ChatGPT is capable of writing and executing Python code, but it doesn't occur to it to use that in cases like this.

Thanks, that was essentially the test. I've gotten into a number of disagreements with people on HN about whether LLMs are 'just' token predictors, whether they 'understand' (whatever we mean by that), whether there's a guiding intelligence, whether they're 'just' language calculators, etc.

As someone else in this thread nicely put it, the tools are being sold as a hop, skip, and jump away from AGI. They clearly aren't. ChatGPT tells us to "ask anything." I did that. There is no 'there' there with these tools. They aren't even dumb.

I have a similar test for image gens. I try to get them to write reversed text in condensation on windows. The new GPT is the best so far, it can sorta, maybe, do it sometimes. Others will sometimes reverse the letter order, but not flip each character.

Try playing a game of Hangman with ChatGPT. It's hilarious.

It does surprisingly well!

Edit: scratch that, it thought there was a six letter word starting with "trs" and then changed its mind to "tre" when I guessed "e." Hilarious.

Just copied your prompt and it handled it just fine.

?siht ekil kool rewsna eht diD

Edit: realized just now that my summary of the 'test' failed to specify the request fully: the letters need to be reversed, too. Maybe I'm just bad with AI tools, because I didn't even get a response that 'this like looked' (i.e. reversed the order of the words).

LLMs work with tokens, not letters. So that's not going to work.

It might work in an agent system where it can make and execute code to solve problems.

By the way, why _do_ llms work with tokens and not letters? Is that some kind of optimization, or is there a more fundamental reason for it?

I actually would love to see the output of an LLM that used letters or syllables or phonemes. The output when it makes mistakes would be absolutely wild.

> best solved with what used to be called symbolic AI before it started working

Right, the current paradigm of requiring an LLM to do arbitrary digit multiplication will not work and we shouldn’t need to. If your task is “do X” and it can be reliably accomplished with “write a python program to do X” that’s good enough as far as I’m concerned. It’s preferable, in fact.

Btw Chollet has said basically as much. He calls them “stored programs” I think.

I think he is onto something. The right atomic to approach these problems is probably not the token, at least at first. Higher level abstraction should be refined to specific components, similar to the concept of diffusion.

As soon as the companies behind these systems stop marketing them as do-anything machines, I will stop judging them on their ability to do everything.

The ChatGPT input field still says ‘Ask anything’, and that is what I shall do.

You can ask me anything. I don’t see that as a promise that I am infallible.

Pricing Schedule

__________________

Answers: $1

Thoughtful Answers: $5

Correct Answers: $50

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> that’s good enough as far as I’m concerned

But in that case, why an LLM. If we want Question-Answer machines to be reliable, they must have the skills which include "counting" just as a basic example.

The purpose of the LLM would be to translate natural language into computer language, not to do the calculation itself.

But in that case, /all/ the LLM would be allowed to do would be to «translate natural language into computer language». And why and how should and could it achieve reliability in that - though not in other realms?

Most human ten year olds in school can add two large numbers together. If a connectionist network is supposed to model the human brain, it should be able to do that. Maybe LLMs can do a lot of things, but if they can't do that, then they're an incomplete model of the human brain.

No LLM or other modern AI architecture I'm aware of is supposed to model the human brain. Even if they were, LLMs can add large numbers with the level of skill I'd expect from a 10 year old:

----

What's 494547645908151+7640745309351279642?

ChatGPT said: The sum of 494,547,645,908,151 and 7,640,745,309,351,279,642 is:

7,641,239,857,997,187,793

----

(7,641,239,856,997,187,793 is the correct answer)

I tried it on gpt-4-turbo and it seems to give the right answer:

>Let's calculate:494,547,645,908,151+7,640,745,309,351,279,642=7,641,239,856,997,187,793 >494,547,645,908,151+7,640,745,309,351,279,642=7,641,239,856,997,187,793 >Answer: 7,641,239,856,997,187,793

If I were to guess, most (adult) humans could not add two 3 digit numbers together with 100% accuracy. Maybe 99%? Computers can already do 100%, so we should probably be trying to figure out how to use language to extract the numbers from stuff and send them off to computers to do the calculations. Especially because in the real world most numbers that matter are not just two digits addition

Artificial neural nets are pretty far from brains. We don’t use them because they are like brains, we use them because they can approximate arbitrary functions given sufficient data. In other words, they work.

For what it’s worth, people are also pretty bad at math compared to calculators. We are slow and error prone. That’s ok.

What I was (poorly) trying to say is that I don’t care if the neural net solves the problem if it can outsource it to a calculator. People do the same thing. What is important is reliably accomplishing the goal.

Most human ten year olds can add two large numbers together with the aid of a scratchpad and a pen. You need tools other than a single dimensional vector of text to do some of these things.

AI apologists need to decide whether they are claiming LLMs are almost-AGI, or not.

This backlash of pointing out LLM failures is a reaction to the overblown hype. We don't expect a statistical-language-processing-gadget to do math well, but then people need to stop claiming they're something other than statistical-language-processing-gadgets.

>> If you want to saturate any model today give it a string and a grammar and ask it to generate the string from the grammar.

I'm not sure I understand what that means - could you explain please?

It means applying specific rules about how text can be generated. For example, generating valid json reliably. Currently we use constrained decoding to accomplish this (e.g. the next token must be one of three valid options).

Now you can imagine giving an LLM arbitrary validity rules for generating text. I think that’s what they mean by “grammar”.

I'm not GP, but here goes:

LLMs are token-based, which are words or word fragments; they have limited ability to work on a letter-by-letter basis. They can't reliably count letters in a sentence, for example. "give it a string and a grammar and ask it to generate the string from the grammar" can't be done by inference alone because of this: they would generate tokens that don't match the grammar.

But you can use a grammar-based sampler and it'll generate valid strings just fine. llama.cpp can easily do this if you provide an EBNF grammar specification.

It's not about the generation, it's about verification.

Changing my tests from the strings I was interested in to four or more letter common words _did_ improve the ability of reasoning LLMs to get the right answer, at the cost of the context exploding to thousands of tokens.

Unfortunately I can't tell you by how much because the couple of dozen tests I did after reading your post ate my $50 I keep in an account for these types of things.

The following question ate through 8k thinking tokens to get the right answer in Claude3.7 Sonnet Extended:

---

Given the following grammar:

    <start> ::= <path>
    <path> ::= Rome <path> | Paris <path> | London <path> | end_path <routes>
    <routes> ::= <path> | end_route <company>
    <company> ::= end_company | <path>

Is the following sentence valid:

Rome Paris Rome end_path Rome London end_path end_company

---

Incidentally it got the right answer no less than 4 times in the thinking token stream. I'd not seen this model act like this before.

Show me the results of your symbolic AI on ARC 2.

ARC 2 is brand new, but neurosymbolic approaches have performed well on the original ARC, e.g. https://arxiv.org/abs/2411.02272

Yet LLMs won on ARC 1. So what’s your point exactly?

> If you don't have an essentially infinite set to draw your validation data from then a large enough model will memorize it as part of its developer teams KPIs.

Sounds like a use-case for property testing: https://en.wikipedia.org/wiki/Software_testing#Property_test...

> I've had _every_ model fail this

That seems to be because LLMs don't seem to be able to follow procedures (e.g. reliably counting).