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Comment by xp84 | Hacker News Reader

Seriously. GPT doing math is like using a 737 to drive around on the ground, or if you had the phone number of a prominent astrophysicist and you call him to do long division for you. Wtf is the point. We have computer things to do every math problem. It’s a waste of energy to use LLMs for it in my opinion.

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"We" are never going to stop trying to use LLMs for math. They are obviously mimicking a smart person. What do you do with smart persons? You query them with bad and lazy questions (often without being too honest about that to yourself), and hope/expect helpful answers.

Because that is how it works. The limiting factor so far has been the smart persons time and patience. Now, no longer.

People moderating their LLMs usage is never happening, from here on out until the end of civilisation. Any LLM service that is designing for that is done. You need to make lazy questions efficient. People do not care about how complicated your sql query is and they will never care. People will not give up on energy, meat, cars, as long as they feel they are giving something up.

People will never think twice to not make your LLM think twice.

If it seems useful and convenient, people will use it. If it's not giving good answers to lazy questions out of the box, they will go to the thing that does.

>"We" are never going to stop trying to use LLMs for math. They are obviously mimicking a smart person.

A lot of words to make a big deal out of nothing. All that is needed is some new abstracted layer that identifies a math question and then proxies it over to the wolfram plugin. That’s it

We don’t have crazy debates over whether a polygon should be rendered by the cpu or a gpu. We solved this problem

So a Mixture of Experts model; but then isn't ChatGPT using that already? Why're the models so bad at math despite being trained on academic papers and books clearly - or why do they hallucinate and make up non-existing citations?

Because they don’t actually reason about things they just map words based on probability. If they’ve seen a math problem enough they may get it right due to probability, but using that knowledge to solve a new problem isn’t likely.

I have bad news about how "solved" CPU/GPU rendering split is.

It’s not about the results, it’s about its ability to “reason”. Math is about as close to pure reasoning we get so I don’t get the pessimism.

If it is bad at math and can’t be taught, then you have a fundamental problem. It’s a matter of time before this limit gets hit in other domains.

In my experience, it can reason usably well but acts like it has dyscalculia. It'll set up a proper algorithm, step through it and trip over digits.

"It'll set up a proper algorithm, step through it and trip over digits."

Or it is just pretending to do so. And since it pretends, of course it trips over all small things as it does not understand them.

I don't think you can pretend to properly describe and evaluate an algorithm, anymore than you can pretend to solve a riddle - the answer is either right or it isn't.

And in this case, the shape of the answer is often right; it just makes ... ordinary errors. Ironically, the AI is a lot better at high-level thinking than correct calculation.

"Ironically, the AI is a lot better at high-level thinking than correct calculation."

That would be actually a human like feature .. except I do not consider what LLMs are doing as thinking.

Yes and the reason it trips over digits is because those digits wind up being tokenized in ways that seem unexpected to us and would produce the same difficulty if we were presented with them:

100,000 + 987 - 1444 * 25,945.842 / 0.0042

becomes

"100" one hundred

"," comma

"000" triple zero

" +" space plus

" 9" space nine

"87" eighty seven

" -" space minus

" 14" space 14

"44" forty four

" *" space times

" 25" space twenty five

"," comma

"9" nine

"45" forty five

"." period

"8" eight

"42" forty two

" /" space divide

" 0" zero

"." period

"00" double zero

"42" forty two

Now imagine someone reading that to you over the phone once and asking you to do the math in your head and you aren't allowed to use paper and pencil and you have to get it right the first time.

This seems fair enough to me. That ChatGPT currently struggles with certain types of maths problems points to reasonably fundamental shortcomings in what otherwise appears to have the beginnings of a general purpose reasoning engine (whether you consider it AGI or not), and I'm willing to bet extraordinarily clever minds are working hard on trying to address those shortcomings.

I don't think that trying to shoehorn LLM into being AGI is the right path. Maybe some are trying to achieve this, or are hoping for this... but IMO this is trying to fit a square peg into a round hole. It's definitely an element in the overall puzzle, I think we can all agree, though. Even so, rather than warping this hammer to also screw in a bolt, why not combine it with another tool more fit for the job?

Your thinking is very emotional and shows your inability to understand that "conversational output" is basically an accidental side-effect of a complex tool that just replicates our speech without understanding what it is saying.

Through continuous use, I have found that it does not "reason". That doesn't mean it's not valuable in many ways, and I have found it to be very helpful in a multitude of diverse applications, including helping me reflect on my own life through my own interpretations of its output. It's also a great interface for JSTOR, wikipedia, and basically any language learning.

I'm having a hard time making the jump from "this must be a calculator" to "this must be a philosopher" to be useful. When did we ever have those requirements for a tool?

This tool is just not made for math. Most of its logic processing abilities seem to surpass mine if I am only given 5 minutes to understand a problem. If you understand the tool, you will get the most out of it. Stop anthropomorphizing it, and stop pretending that it can't generate both highly beneficial or highly harmful content simply because it doesn't have a soul/d*ck or whatever.

> GPT doing math is like using a 737 to drive around on the ground, or if you had the phone number of a prominent astrophysicist and you call him to do long division for you.

I don't think this is a great analogy. if your 737 couldn't drive on the ground and your astrophysicist couldn't answer basic maths questions I wouldn't want to fly in that plane or put much faith in the astrophysicists answers to more complex questions.

Maybe maths is not a particular strength of LLMs, but asking questions where it is easy to judge the factual accuracy of the responses seems a pretty reasonable test to be running.

> asking questions where it is easy to judge the factual accuracy of the responses seems a pretty reasonable test to be running.

It isn't reasonable if that isn't what the system was designed to do.

It would be a poor test of my general practitioner's competence to ask him calculus questions and conclude he doesn't know what he's talking about because he can't answer them.

Given that a drug's concentration in your bloodstream is absolutely critical for prescribing them, and considering that this is calculated using Calculus, you'd better hope your GP does actually know their Calculus!

My point was that a 737 can be a land vehicle, but very badly, because it's optimized (to an extreme amount) for flying. I fly on 737s all the time, knowing that have terrible stopping distances and cornering. The 0-60 could be decent, but you can't really accelerate all-out to try it, or you'll overshoot and end up going 175 and crashing.

The astrophysicist can do long division in his head, but he'll be about as fast and accurate as the next person, because he doesn't practice arithmetic every day.

I agree with the commenter somewhere in my thread who said all an LLM should be optimized for is to classify the type of problem and feed it into a purpose-built, deterministic solver that is trained to interpret math as math and not as language, be it ML-based or algorithmic.

A better example would be - calling your guitar lessons teacher for help on a statistics problem.

Possibly more like instantiating the statistics problem by getting, say, every 3 members of an orchestra to represent a triplet of doors in the Monty Hall problem and then making a ball-park guess what the results are from a seat in the middle of the back row.

The point of doing $non-llm-optimal-thing on an llm is the hope that it let's you skip on formal syntaxes, which are mentally taxing.

It's far easier even for an expert to communicate what they want in natural language than it is in a formal syntax for all but the most trivial things.

It should be a goal of these tools to do this correctly.

> the hope that it let's you skip on formal syntaxes, which are mentally taxing

agree completely, but the LLM should be focused, then, strictly on formulating an "execution plan" of sorts and handing that off, not on performing math itself.

In other words, when asked "if i have 349 blueberries and one blueberry turns into a cherry per hour, how many of each fruit will I have in 93478 minutes?" it shouldn't be doing the actual arithmetic, but it should be figuring out what arithmetic would need to be done.

This is my second ChatGPT comment so I hate to sound like an evangelist, but it basically does this, reproducibly:

You describe a complex relationship between several nodes(people, cities, etc), and then ask ChatGPT to draw the relationship as a graph data structure. It will create a formula that Mathematica can render, and then send the formula to Mathematica before presenting an ascii drawing. Usually. Sometimes it just complains that it can't draw and explains what the graph looks like to you in a written formal/human-readable syntax.

In other words, yes it just summarizes the problem, converts it to a formal syntax, and sends it off to some other tool.

> Seriously. GPT doing math is like using a 737 to drive around on the ground, or if you had the phone number of a prominent astrophysicist and you call him to do long division for you. Wtf is the point. We have computer things to do every math problem. It’s a waste of energy to use LLMs for it in my opinion.

Using GPT to do maths is probably like using a 737 to drive around on the ground.

Teaching GPT to do maths might be like teaching a child the times tables - a skill that can help overall reasoning.

Apparently, models finetuned for coding are better at logical inquiries than those that are not.

Well that's... a claim. What's your reasoning for operations on digits within the model itself improving any current metrics?

Well I didn't really make a bold claim, I said "might", but presumably there is a reason why we teach kids basic mental maths before we teach them how to use a calculator.

Humans are different to an AI, but putting that aside, my intuition would be that if we never taught kids any mental maths, their concept/understanding of numbers would be fundamentally different to how it is if they learn that 9 x 9 = 81 (also look at how your fingers move - there is a relationship there!).

But who knows, AI is strange and there's lots of stuff that needs to be experimented with. I would think training an intuitive sense of numbers would have other fall-outs though. This is half the beauty of LLM's right? You show an LLM some history books and it also learns about biology, politics, grammar, etymology and love. You teach a LLM maths and it also learns ... ?

> GPT doing math is like using a 737 to drive around on the ground, or if you had the phone number of a prominent astrophysicist and you call him to do long division for you.

One significant difference is that in both of those examples it is (or quickly becomes) plain why it’s a ridiculous idea. Even if you don’t understand it yourself, you’ll get external feedback fast. Not so with LLMs, where even people with technical needs may fail to see what is or isn’t a good use of the tool. Case in point: https://news.ycombinator.com/item?id=36782446

“You’re holding it wrong” isn’t a valid argument in perpetuity. At a certain point it becomes the fault of the designer, not the user.

My guess is that for most folks good at math => you're smart, so the most obvious way to test an LLM is to ask it a math question, which is trivial to construct. Another motivation IMO is that asking a relatively simple math question and seeing an LLM fall flat kind of makes many to feel good about themselves and gives them a chance to show it off on social media.

Asking models to do math is kind of an effecitve way to measure their capabilities, especially in reasoning and abstraction, which are quite important for problem solving.

You don't need to reason and abstraction to do basic calculation. ChatGPT will however happily give you some decent answers about not-too -hard math that requires reasoning. It just won't operate on digits.

Those are completely different ideas.

What is the point of doing anything if you can't use flashy technologies ? Leave that to the old people. /s

I used GPT-4 to generate a non-cryptographic random 64 character string. It was faster to ask GPT-4 for the string than ask GPT-4 for the instructions to generate the string from my terminal. GPT-4 was faster than google.

Surely navigating to random.org is faster than typing a prompt.

That definitely won't be random.

It's probably 4, that's pretty random.

Close enough for the purpose at hand!

That’s likely a very good example of the limitations of LLMs.

I don't think this will be a truly random string.

Were you in a room that had it's walls slowly caving in like in indiana jones or why does it matter that it was faster ?

Close! Was in a meeting and needed to see the behavior of a system when passed a string greater than 64 char.

My mental capacity was used elsewhere - using chatGPT let me answer an important customer question authoritatively.

wouldn't it be less time to just type one yourself? Like open notepad, hit the keys like a deranged monkey, select the first 64 characters, done?

That is very much not random, but generally enough for 99% of all use cases.

I would not bet on whether chatgpt's results would be more or less random than this process.

Aren't chatgpts results deterministic if you use the same seed, temperature and other parameters?

It would be more random than an LLM's output

Obligatory bash solution:

  $ random_bytes() { xxd -plain -c 0 -l "$1" /dev/urandom; }
  $ random_bytes 32
  e6a4a7bbea69a0164cbb66c89f8f528af93c6d2459fd28d2640e2952c031b618

>/dev/urandom

Hmm, I’ve been using /usr/games/fortune

Is this not a best practice?

I'm glad ChatGPT exists so I don't have to remember any of that

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