Over just a few months, ChatGPT went from correctly answering a simple math problem 98% of the time to just 2%, study finds. Researchers found wild fluctuations—called drift—in the technology’s abi…::ChatGPT went from answering a simple math correctly 98% of the time to just 2%, over the course of a few months.
This paper is pretty unbelievable to me in the literal sense. From a quick glance:
First of all they couldn’t even bother to check for simple spelling mistakes. Second, all they’re doing is asking whether a number is prime or not and then extrapolating the results to be representative of solving math problems.
But most importantly I don’t believe for a second that the same model with a few adjustments over a 3 month period would completely flip performance on any representative task. I suspect there’s something seriously wrong with how they collect/evaluate the answers.
And finally, according to their own results, GPT3.5 did significantly better at the second evaluation. So this title is a blatant misrepresentation.
Also the study isn’t peer-reviewed.
It’s a machine learning chat bot, not a calculator, and especially not “AI.”
Its primary focus is trying to look like something a human might say. It isn’t trying to actually learn maths at all. This is like complaining that your satnav has no grasp of the cinematic impact of Alfred Hitchcock.
It doesn’t need to understand the question, or give an accurate answer, it just needs to say a sentence that sounds like a human might say it.
This. It is able to tap in to plugins and call functions though, which is what it really should be doing. For math, the Wolfram alpha plugin will always be more capable than chatGPT alone, so we should be benchmarking how often it can correctly reformat your query, call Wolfram alpha, and correctly format the result, not whether the statistical model behind chatGPT happens to use predict the right token
It sounds like it’s time to merge Wolfram Alpha’s and ChatGPT’s capabilities together to create the ultimate calculator.
to be fair, fucking up maths problems is very human-like.
I wonder if it could also be trained on a great deal of mathematical axioms that are computer generated?
It doesn’t calculate anything though. You ask chatgpt what is 5+5, and it tells you the most statistically likely response based on training data. Now we know there’s a lot of both moronic and intentionally belligerent answers on the Internet, so the statistical probability of it getting any mathematical equation correct goes down exponentially with complexity and never even approaches 100% certainty even with the simplest equations because 1+1= window.
i know it doesn’t calculate, that’s why I suggested having known correct calculations in the training data to offset noise in the signal?
If it’s trying emulate a human then it’s spot on. I suck at maths.
so it confidently spews a bunch of incorrect shit, acts humble and apologetic while correcting none of its behavior, and constantly offers unsolicited advice.
I think it trained on Reddit data
acts humble and apologetic
We must be using different Reddits, my friend
You’re right, but at least the satnav won’t gaslight you into thinking it does understand Alfred Hitchcock.
GPT was always really bad at math.
I’ve asked it word problems before and it fails miserably, giving me insane answers that make no sense. For example, I was curious once how many stars you would expect to find in a region of the milky way with a radius of 650 light years, assuming an average of 4 light years per star. The first answer it gave me was like a trillion stars or something, and I asked it if that makes sense to it, a trillion stars in a subset of space known to only contain about a quarter of that number, and it gave me a wildly different answer. I asked it to check again and it gave me a third wildly different number.
Sometimes it doubles down on wrong answers.
GPT is amazing but it’s got a long way to go.
It seems rather suspicious how much ChatGPT has deteorated. Like with all software, they can roll back the previous, better versions of it, right? Here is my list of what I personally think is happening:
- They are doing it on purpose to maximise profits from upcoming releases of ChatGPT.
- They realized that the required computational power is too immense and trying to make it more efficient at the cost of being accurate.
- They got actually scared of it’s capabilities and decided to backtrack in order to make proper evaluations of the impact it can make.
- All of the above
- It isn’t and has never been a truth machine, and while it may have performed worse with the question “is 10777 prime” it may have performed better on “is 526713 prime”
ChatGPT generates responses that it believes would “look like” what a response “should look like” based on other things it has seen. People still very stubbornly refuse to accept that generating responses that “look appropriate” and “are right” are two completely different and unrelated things.
In order for it to be correct, it would need humans employees to fact check it, which defeats its purpose.
It really depends on the domain. Asking an AI to do anything that relies on a rigorous definition of correctness (math, coding, etc) then the kinds of model that chatGPT just isn’t great for that kinda thing.
More “traditional” methods of language processing can handle some of these questions much better. Wolfram Alpha comes to mind. You could ask these questions plain text and you actually CAN be very certain of the correctness of the results.
I expect that an NLP that can extract and classify assertions within a text, and then feed those assertions into better “Oracle” systems like Wolfram Alpha (for math) could be used to kinda “fact check” things that systems like chatGPT spit out.
Like, it’s cool fucking tech. I’m super excited about it. It solves pretty impressively and effiently a really hard problem of “how do I make something that SOUNDS good against an infinitely variable set of prompts?” What it is, is super fucking cool.
Considering how VC is flocking to anything even remotely related to chatGPT-ish things, I’m sure it won’t be long before we see companies able to build “correctness” layers around systems like chatGPT using alternative techniques which actually do have the capacity to qualify assertions being made.
That’s not necessarily true: https://arstechnica.com/google/2023/06/googles-bard-ai-can-now-write-and-execute-code-to-answer-a-question/. If the question gets interpreted correctly and it manages to write working code to answer it, it could correctly answer questions that it has never seen before.
They are lobotomizing the softwares ability to provide bad PR answers which is having cascading effects via a skewed data set.
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This is what was addressed at the start of the comment, you can just roll back to a previous version. It’s heavily ingrained in CS to keep every single version of your software forever.
I think that there is another cause. Remember the screenshots of users correcting chatgpt wrongly? I mean chatgpt takes user’s inputs for it’s benefit and maybe too much of these wrong and funny inputs and chatgpt’s own mistake of not regulating what it should take in and what it should not might be an additional reason here.
HMMMM. It’s almost like it’s not AI at all, but just a digital parrot. Who woulda thought?! /s
To it, everything is true and normal, because it understands nothing. Calling it “AI” is just for compromising with ignorant people’s “knowledge” and/or for hype.
Exactly. It should be called ML model, because that’s what it is, and I’ll just keep calling that. Everyone should do that.
What does that stand for? O:
You’d think I’d know that since I’m talking about AI; but actually most of my knowledge is about how things work or don’t work, not current trends/news.
ML stands for machine learning
Ah, thanks! ^^
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I used GPT4 the other day and it worked perfectly for calculating formulas of straight lines on linear-log plots but maybe I was the 2%
Why are people using a language model for math problems?
It was initially presented as the all-problem-solver, mainly by the media. And tbf, it was decently competent in certain fields.
Problem was it was presented as problem solved which it never was, it was problem solution presenter. It can’t come up with a solution, only come up with something that looks like a solution based on what input data had. Ask it to invert sort something and goes nuts.
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I used Wolfram Alpha a lot in college (adult learner, but that was about ~4 years ago that I graduated, so no idea if it’s still good). https://www.wolframalpha.com/
I would say that Wolfram appears to probably be a much more versatile math tool, but I also never used chatgpt for that use case, so I could be wrong.
“AI” taking our jobs and all that huh
My personal pet theory is that a lot of people were doing work that involved getting multiple LLMs in communication. When those conversations were then used in the RL loop we start seeing degradation similar to what’s been in the news recently with regards to image generation models. I believe this is the paper that got everybody talking about it recently: https://arxiv.org/pdf/2307.01850.pdf
This is peer-reviewed? they use a line in the discussion which seems relatively unprofessional, telling people to join a 12-step program if they like to use artificial training data.
Not affiliated with the paper in any way. Have just been following the news around it.
ArXiv papers are never peer reviewed.
Thank you
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I don’t understand why anyone even considers that. It’s a toy. A novelty, a thing you mess with when you’re bored and want to see how Hank Hill would explain the plot of Full Metal Alchemist, not something you would entrust anything significant to.
These models are black boxes right now, but presumably we could open it up and look inside to see each and every function the model is running to produce the output. If we are then able to see what it is actually doing and fix things up so we can mathematically verify what it does will be correct, I think we would be able to use it for mission critical applications. I think a more advanced LLM likes this would be great for automatically managing systems and to do science+math research.
But yeah. For right now these things are mainly just toys for SUSSY roleplays, basic customer service, and generating boiler plate code. A verifiable LLM is still probably 2-4 years away.
The problem is if you open it up, you just get trillions of numbers. We know what each function does, it takes a set of numbers between -1 and 1 that other nodes passed it, adds them up, checks if the sum is above or below a set threshold, and passes one number to the next nodes if it’s above and one if it’s below, some nodes toss in a bit of random variance to shake things up. The black box part is the fact that there are trillions of these numbers and they have no meaning individually.
