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alexwebb2

Demonstrably false.

https://chatgpt.com/share/6722ca8a-6c80-800d-89b9-be40874c5b...

https://chatgpt.com/share/6722ca97-4974-800d-99c2-bb58c60ea6...

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It's worth noting that this may not be result of a pure LLM, it's possible that ChatGPT is using "actions", explicitly:

1- running the query through a classifier to figure out if the question involves numbers or math 2- Extract the function and the operands 3- Do the math operation with standard non-LLM mechanisms 4- feed back the solution to the LLM 5- Concatenate the math answer with the LLM answer with string substitution.

So in a strict sense this is not very representative of the logical capabilities of an LLM.

It shows you when it's calling functions. I also did the same test with Llama, which runs locally and cannot access function calls and it works.

You are right I actually downloaded Llama to do more detailed tests. God bless Stallman.

Then what's the point of ever talking about LLM capabilities again? We've already hooked them up to other tools.

This confusion was introduced at the top of the thread. If the argument is "LLMs plus tooling can't do X," the argument is wrong. If the argument is "LLMs alone can't do X," the argument is worthless. In fact, if the argument is that binary at all, it's a bad argument and we should laugh it out of the room; the idea that a lay person uninvolved with LLM research or development could make such an assertion is absurd.

Minor edits to well known problems do easily fool current models though. Here's one 4o and o1-mini fail on, but o1-preview passes. (It's the mother/surgeon riddle so kinda gore-y.)

https://chatgpt.com/share/6723477e-6e38-8000-8b7e-73a3abb652...

https://chatgpt.com/share/6723478c-1e08-8000-adda-3a378029b4...

https://chatgpt.com/share/67234772-0ebc-8000-a54a-b597be3a1f...

I think you didn't use the "share" function; I cannot open any of these links. Can you do it in a private browser session (so you're not logged in)?

Oops, fixed the links.

mini's answer is correct, but then it forgets that fathers are male in the next sentence.

At this point I really only take rigorous research papers in to account when considering this stuff. Apple published research just this month that the parent post is referring to. A systematic study is far more compelling than an anecdote.

https://machinelearning.apple.com/research/gsm-symbolic

That study shows 4o, o1-mini and o1-preview's new scores are all within margin error on 4/5 of their new benchmarks(some even see increases). The one that isn't involves changing more than names.

Changing names does not affect the performance of Sota models.

>That study very clearly shows 4o, o1-mini and o1-preview's new scores are all within margin error on 4/5 of their new benchmarks.

Which figure are you referring to? For instance figure 8a shows a -32.0% accuracy drop when an insignificant change was added to the question. It's unclear how that's "within the margin of error" or "Changing names does not affect the performance of Sota models".

Table 1 in the Appendix. GSM-No-op is the one benchmark that sees significant drops for those 4 models as well (with preview dropping the least at -17%). No-op adds "seemingly relevant but ultimately inconsequential statements". So "change names, performance drops" is decidedly false for today's state of the art.

Thanks. I wrongly focused on the headline result of the paper rather than the specific claim in the comment chain about "changing name, different results".

Ah, that’s a good point thanks for the correction.

Only if there isn’t a systemic fault, eg bad prompting.

Their errors appear to disappear when you correctly set the context from conversational to adversarial testing — and Apple is actually testing the social context and not its ability to reason.

I’m just waiting for Apple to release their GSM-NoOp dataset to validate that; preliminary testing shows it’s the case, but we’d prefer to use the same dataset so it’s an apples-to-apples comparison. (They claim it will be released “soon”.)

To be fair, the claim wasn't that it always produced the wrong answer, just that there exists circumstances where it does. A pair of examples where it was correct hardly justifies a "demonstrably false" response.

Conversely, a pair of examples where it was incorrect hardly justifies the opposite response.

If you want a more scientific answer there is this recent paper: https://machinelearning.apple.com/research/gsm-symbolic

It kind of does though, because it means you can never trust the output to be correct. The error is a much bigger deal than it being correct in a specific case.

You can never trust the outputs of humans to be correct but we find ways of verifying and correcting mistakes. The same extra layer is needed for LLMs.

> It kind of does though, because it means you can never trust the output to be correct.

Maybe some HN commenters will finally learn the value of uncertainty then.

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