short answer: no.

Long Answer: They are still (mostly) statisics based and can't do real math. You can use the answers from LLMs as starting point, but you have to rigerously verify the answers they give.

Why would I bother?

Calculators exist, logic exists, so no... LLMs are a laughably bad fit for directly doing math, they are bullshit engines they cannot "store" a value without fundamentally exposing it to hallucinating tendencies which is the worst property a calculator could possibly have.

No, thank you for coming to my TED Talk.

Vibe math. No thank you

Just use Wolfram Alpha instead

I wouldn't bother. If I really had to ask a bot, Wolfram Alpha is there as long as I can ask it without an AI meddling with my question.

E: To clarify, just because one AI or six will get the same answer that I can independently verify as correct for a simpler question, does not mean I can trust it for any arbitrary math question even if however many AIs arrive at the same answer. There's often the possibility the AI will stumble upon a logical flaw, exemplified by the "number of rs in strawberry" example.

Yes, with absolute certainty.

For example: 2 + 2 = 5

It's absolutely correct and if you dispute it, big bro is gonna have to re-educated you on that.

LLMs don't and can't do math. They don't calculate anything, that's just not how they work. Instead, they do this:

2 + 2 = ? What comes after that? Oh, I remember! It's '4'!

It could be right, it could be wrong. If there's enough pattern in the training data, it could remember the correct answer. Otherwise it'll just place a plausible looking value there (behavior known as AI hallucination). So, you can not "trust" it.

Would you trust six mathematicians who claimed to have solved a problem by intuition, but couldn’t prove it?

That’s not how mathematics works: if you have to “trust” the answer, it isn’t even math.

Here's an interesting post that gives a pretty good quick summary of when an LLM may be a good tool.

Here's one key:

Machine learning is amazing if:

• The problem is too hard to write a rule-based system for or the requirements change sufficiently quickly that it isn't worth writing such a thing and,
• The value of a correct answer is much higher than the cost of an incorrect answer.

The second of these is really important.

So if your math problem is unsolvable by conventional tools, or sufficiently complex that designing an expression is more effort than the answer is worth... AND ALSO it's more valuable to have an answer than it is to have a correct answer (there is no real cost for being wrong), THEN go ahead and trust it.

If it is important that the answer is correct, or if another tool can be used, then you're better off without the LLM.

The bottom line is that the LLM is not making a calculation. It could end up with the right answer. Different models could end up with the same answer. It's very unclear how much underlying technology is shared between models anyway.

For example, if the problem is something like, "here is all of our sales data and market indicators for the past 5 years. Project how much of each product we should stock in the next quarter. " Sure, an LLM may be appropriately close to a professional analysis.

If the problem is like "given these bridge schematics, what grade steel do we need in the central pylon?" Then, well, you are probably going to be testifying in front of congress one day.

Using a calculator or wolfram alpha or similar tools i don't trust the answer unless it passes a few sanity checks. Frequently I am the source of error and no LLM can compensate for that.

Most LLM's now call functions in the background. Most calculations are just simple Python expressions.

Nope, language models by inherent nature, xannot be used to calculate. Sure theoretically you could have input parsed, with proper training, to find specific variables, input those to a database and have that data mathematically transformed back into language data.

No LLMs do actual math, they only produce the most likely output to a given input based on trained data. If I input: What is 1 plus 1?

Then given the model, most likely has trained repetition on an answer to follow that being 1 + 1 = 2, that will be the output. If it was trained on data that was 1 + 1 = 5, then that would be the output.

no, once i tried to do binary calc with chat gpt and he keot giving me wrong answers. good thing i had sone unit tests around that part so realised quickly its lying

I’ve used LLMs quite a few times to find partial derivatives / gradient functions for me, and I know it’s correct because I plug them into a gradient descent algorithm and it works. I would never trust anything an LLM gives blindly no matter how advanced it is, but in this particular case I could actually test the output since it's something I was implementing in an algorithm, so if it didn't work I would know immediately.

Well, I wanted to know the answer and formula for future value of a present amount. The AI answer that came up was clear, concise, and thorough. I was impressed and put the formula into my spreadsheet. My answer did not match the AI answer. So I kept looking for what I did wrong. Finally I just put the value into a regular online calculator and it matched the answer my spreadsheet was returning.

So AI gave me the right equation and the wrong answer. But it did it in a very impressive way. This is why I think it's important for AI to only be used as a tool and not a replacement for knowledge. You have to be able to understand how to check the results.

this is a really weird premise. doing the same thing on 6 models is just not worth it especially when wolfram alpha exists and is far more trustable and speedy

No lol. I don't trust a calculator to write me text and not a auto complete to solve me math problems

no, LLM's are designed to drive up user engagement nothing else, it's programmed to present what you want to hear not actual facts. plus it's straight up not designed to do math

no

Maybe? I'd be looking all over for some convergent way to fuck it up, though.

If it's just one model or the answers are only close, lol no.

No. Dear God no. Llms are not computers. They are just prediction machines. They predict that the next value is probably this value. There is no actual math there.

You cannot trust LLMs. Period.

They are literally hallucination machines that just happen to be correct sometimes.

No because there is randomness involved

How trustable the answer is depends on knowing where the answers come from, which is unknowable. If the probability of the answers being generated from the original problem are high because it occurred in many different places in the training data, then maybe it's correct. Or maybe everyone who came up with the answer is wrong in the same way and that's why there is so much correlation. Or perhaps the probability match is simply because lots of math problems tend towards similar answers.

The core issue is that the LLM is not thinking or reasoning about the problem itself, so trusting it with anything is more assuming the likelihood of it being right more than wrong is high. In some areas this is safe to do, in others it's a terrible assumption to make.

Use Wolfram Alpha for mathematics

Never a base model, absolutely with an agent and function calling with a properly made tool and retrieval.

L-L-Mentalist!

For practice yeah as there is usually something you can do to verify the value. For study no as you would not learn shit.

I mean, I don't know why you wouldn't just use something other than an LLM in that case

If you really want to see how good they are, have them do the full calculation for 30! (30 factorial) And see how close it gets to real numbers.

Probably, depending on the context. It is possible that all 6 models were trained on the same misleading data, but not very likely in general.

Number crunching isn't an obvious LLM use case, though. Depending on the task, having it create code to crunch the numbers, or a step-by-step tutorial on how to derive the formula, would be my preference.