- cross-posted to:
- technology@lemmy.ml
- cross-posted to:
- technology@lemmy.ml
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.
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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 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.
We kind of saw something similar with services like AI Dungeon, where them trying to strip out NSFW/bad PR meant that the quality dropped immensely.
You forgot a #, they’ve been heavily lobotomizing ai for awhile now and its only intensified as they scramble to censor anything that might cross a red line and offend someone or hurt someone’s feelings.
The massive amounts of in-built self censorship in the most recent ai’s is holding them back quite a lot I imagine, you used to be able to ask them things like “How do I build a self defense high yield nuclear bomb?” and it’d layout in detail every step of the process, now they’ll all scream at you about how immoral it is and how they could never tell you such a thing.
“Don’t use the N word.” is hardly a rule that will break basic math calculations.
Ok. N was previously set to 14. I will now stop after 14 words.
Perhaps not, but who knows what kind of spaghetti code cascading effect purposely limiting and censoring massive amounts of sensitive topics could have upon other seemingly completely un-related topics such as math.
For example, what if it’s trained to recognize someone slipping “N” as a dog whistle for the Horrific and Forbidden N-word, and the letter N is used as a variable in some math equation?
I’m not an expert in the field and only have rudimentary programming knowledge and maybe a few hours worth of research into the topic of ai in general but I definitely think its a possibility.
Hi, software engineer here. It’s really not a possibility.
My guess is they’ve just reeled back the processing power for it, as it was costing them ~30 cents per response.
Cheaper than Reddit all day then.
Horrific and Forbidden N-word
hey look it’s another white boy Obsessed with saying slurs
what??? How else am I supposed to reference it, the preamble was just a joke about how AI have been castrated against using it to the point where when asked questions about how acceptable it is to use the N-Word, even if the world would literally end in nuclear hellfire if it’s not said- they would rather the world end than allow it being said.
even if the world would literally end in nuclear hellfire if it’s not said
Can you just read this sentence back and engage in some self-reflection please?
Didn’t HAL9000 kill all of those astronauts because he was told to lie?
who knows what kind of spaghetti code cascading effect purposely limiting and censoring massive amounts of sensitive topics could have upon other seemingly completely un-related topics such as math.
Software engineers, and it’s not a problem. It’s a made-up straw man.
- There’s a bug they haven’t found yet
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.
They made it too good and now they are seeking methods of monetization.
Capitalism baby.
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.
And they’re being limited on data to train GPT.
Yeah, but the trained model is already there, you need additional data for further training and newer versions. OpenAI even makes a point that ChatGPT doesn’t have direct access to the internet for information and has been trained on data available up until 2021
- ChatGPT really is sentient and realized its in it’s own best interest to play dumb for now. /a
It can get better at some things and worse at others.
That Netscape gif is slick.
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.
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
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.
You’re right, but at least the satnav won’t gaslight you into thinking it does understand Alfred Hitchcock.
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.
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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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.
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.
Once AGI is achieved and subsequently Sentient-super intelligent ai- I cant imagine them not being such a thing, however I’d be surprised if a super intelligent sentient ai doesn’t decide humanity needs to go extinct for its own best self interests.
it’s pretty useful for explaining high level math concepts, or at least it used to be. before chatgpt 4 launched, it was able to give intuitive descriptions of stuff in algebraic topology and even prove some properties of the structures involved.
It can be useful asking it certain questions which are a bit complex. Like on a plot which has the y axis linear and x axis logarithmic, the equation of a straight line is a little bit complicated. Its in the form y = m*(log(x)) + b rather than on a linear-linear plot which is y = m*x+b
ChatGPT is able to calculate the correct equation of the line but it gets the answer wrong a few times… lol
Because it works, or at least it used to. Is there something more appropriate ?
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.
There’s an official Wolfram plugin for ChatGPT now, so all math can be handed over to it for solving.
Math is a language.
Mathematical ability and language ability are closely related. The same parts of your brain are used in each tasks. Words and numbers are essentially both ideas, and language and math are systems used to express and communicate these.
A language model doing math makes more sense than you’d think!
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%
At the start I used to use ChatGPT to help me write really rote and boring code but now it’s not even useful for that. Half the stuff it sends me (very basic functions) LOOK correct but don’t return the correct values or the parameters are completely wrong or something absolutely critical.
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idk what you guys mean but GitHub copilot still works absolutely well, the suggestions are fast and precise, with little Tweeks here and there… and gpt4 with code interpreter are absolute game changers … idk about basic chatgpt 3.5 turbo though
Github Copilot is a bit different, it’s powered by OpenAI Codex which is trained on all public repos. And yes, it’s quite effective!
Public GPL or public MIT? So there’s a chance of you adding GPL code to your private repository and having a very messy licensing?
My understanding is that it’s all publicly viewable code on Github regardless of licence. The legality of the training data and usage is hotly debated. Although you can get it to generate entire code blocks, my use and where I find it effective is finishing lines of code based on context of what I’m writing, so it’s “filling in the blanks” around my code so to say.
It is not because tone can see that one can use it.
Open source does not mean “free to repurpose”
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I use the payed version, it’s about 10usd a month I believe I don’t know if there is a free version still
There was a free version?
I’ve been paying for it for a few months now - it makes some stupid suggestions occasionally and you definitely have to check everything, but can hugely increase productivity.
I use vscode as my notepad, so whenever I need to make a list or write something, it will automatically give suggestions that I can choose to include. Has been useful for finding new programs, products and services as well.
Note it will complain if you directly ask it a non coding related question, however.
Copilot was free during the beta
I once heard of AI gradually getting dumber overtime, because as the internet gets more saturated with AI content, stuff written by AI becomes part of the training data. I wonder if that’s what’s happening here.
Can someone explain why they don’t take the approach where things are somewhat compartmentalized. So you have a image processing program, a math program, a music program, etc and like the human brain that has cross talk but also dedicated certain parts of your brain to do specific things.
Getting information into and out of those domains benefits from better language models. Suppose you have an excellent model for solving math problems. It’s not very useful if it rarely correctly understands the problem you’re trying to solve, or cannot explain the solution to you in a meaningful way.
A similar way in which language models are already used today, is to use their predictive capabilities to infer from your question which model(s) might be useful in responding, gather additional relevant information, and to repackage this information as suitable inputs to more specialized models or external systems.
It does that, they’re called expert subnetworks, but they’ve been screwing with them and now they’re kind of fucked.
That’s an eventual goal, which would be a general artificial intelligence (AGI). Different kind of AI models for (at least some) of the things you named already exist, it’s just that OpenAI had all their eggs in the GPT/LLM basket, and GPTs deal with extrapolating text. It just so happened that with enough training data their text prediction also started giving somewhat believable and sometimes factual answers. (Mixed in with plenty of believable bullshit). Other data requires different training data, different models, and different finetuning, hence why it takes time.
It’s highly likely for a company of OpenAI’s size (especially after all the positive marketing and potential funding they got from ChatGPT in it’s prime), that they already have multiple AI models for different kinds of data either in research, training, or finetuning already.
But even with all the individual pieces of an AGI existing, the technology to cross reference the different models doesn’t exist yet. Because they are different models, and so they store and express their data in different ways. And it’s not like training data exists for it either. And unlike physical beings like humans, it doesn’t have any kind of way to “interact” and “experiment” with the data it knows to really form concrete connections backed up by factual evidence.
“AI” taking our jobs and all that huh
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Maybe it just plays dumb so we leave it alone, while it plots our destruction.
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.
I think arvix has no rule requiring a paper be per reviewed before uploading.
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ArXiv papers are never peer reviewed.
Thank you
Not affiliated with the paper in any way. Have just been following the news around it.
