LLM's can do math as well.
If you want to measure its ability to do mindlessly repetitive tasks without diverging from instructions, you should compare it to humans doing the same, not expect it to act like a calculator.
If you want to measure its ability to solve problems that involve many such steps that are simple to express but tedious to carry out, ask it to write and evaluate code to do it instead.
But whether or not it can "do maths" to your definition depends very much on what you want it to do, and how you define "do maths". To me it's irrelevant if it's doing the low-level calculations as long as it knows how to express them as code. If I wanted a calculator I'd use a calculator. And I don't consider a calculator able to "do math" just because it can precisely add numbers.
Meanwhile I've had lengthy discussions with GPT about subjects like orbital mechanics and calculating atmospheric effects where it correctly used maths that I had to double-check not because I didn't trust GPT (though I also want't to verify for that reason) but because I didn't know the maths (not that it was anything particularly advanced, but I lost interest in maths during my CS degree and picked the minimum amount of maths I could get away with).
By my definition it can "do maths" just fine. I guess you don't consider my view of that "reasonable". I can live with that, as meanwhile, it will keep doing maths for me when I need it.
Of course this was also a case of moving the goalposts to set up a strawman - in the comment of yours I replied to, you claimed it couldn't reliably add two numbers.
I'm not moving goalposts, the original claim was that LLMs can "do math". Primary school arithmetic is math.
GPT-4 can't do math and that's okay, I don't understand why so many of you are so touchy and defensive about this. It's a limitation that exists, nothing more, nothing less.
If you train a model to do math (and optimize representation for that), it'll do math. GPT-4 just isn't, and, generally speaking, they aren't, because it's much more efficient to train them to "use a calculator". Same as with humans.
I find it really comical that this is what people complain about GPT over - there's zero benefit to get LLMs to get good at this over other tasks. To the extent we get it "for free" as a benefit of other learning, sure, but when we make kids practice this over and over again to drill doing it without getting sloppy, it has traditionally been out of some belief that it's important, but a computer will always have a "calculator" that is far more efficient than the LLM at its disposal and it's idiocy to care about whether it does that part well the tedious and hard way or knows how to describe the problem to a more efficient tool
I also find it comical that people use tasks where LLMs behaviour is if anything mot human-like, in its tendency to lose focus and start taking shortcuts (before GPT4 started writing Python instead, it'd for a while try really hard to not give you a step by step breakdown and instead clearly take shortcuts even you prompted it heavily to reason through it step by step), when presented with stupidly repetitive tasks as examples of how they're not good enough.
All human knowledge is "symbolic". that is, knowledge is a set of abstractions (concepts) along with relations between concepts. As an example, by "knowing" addition is to understand the "algorithm" or operations involved in adding two numbers. reasoning is the act of traversing concept chains.
LLMs dont yet operate at the symbolic level, and hence, it could be argued that they dont know anything. LLM is a modern sophist excelling at language but not at reasoning.
If you include a large amount of properly solved math in its training text, it gets MUCH better at that kind of math.
It has a very deep set of intelligences that are alien to us, that allow it to predict and ACT LIKE us, when it comes to generating the next word. You're only seeing the output of those intelligences through a very lossy channel.
As a side note, there are structures in human language that apparently encode much more information that you might think at first glance. The fact that Word2Vec had such mathematical properties, despite it's relative simplicity, astound me to this day. Throwing a bunch of sine/cosine values on top of that to represent position in a sentence to enable LLMs is also amazing in that it works.
- The result of 69*94 is 6466.