Researchers found that ChatGPT’s performance varied significantly over time, showing “wild fluctuations” in its ability to solve math problems, answer questions, generate code, and do visual reasoning between March and June 2022. In particular, ChatGPT’s accuracy in solving math problems dropped drastically from over 97% in March to just 2.4% in June for one test. ChatGPT also stopped explaining its reasoning for answers and responses over time, making it less transparent. While ChatGPT became “safer” by avoiding engaging with sensitive questions, researchers note that providing less rationale limits understanding of how the AI works. The study highlights the need to continuously monitor large language models to catch performance drifts over time.
My understanding is this claim is basically entirely false. The tests done by these researchers had some glaring errors that when corrected, show gpt-4 is getting slightly better at math, if anything. See this video that describes some of the issues: https://youtu.be/YSokS2ivf7U
TL;DR The researchers gave new GPT questions from two different pools. It’s no surprise they got worse answers.
You shouldn’t need to be a prompt engineer just to get answers to math questions that are not blatantly wrong. I believe the prompts are included in the paper so that you don’t have to guess if they were badly formatted.
The problem is they aren’t comparing apples to apples. They asked each version of GPT a different pool of questions. (Edited my post to make this clear).
Once you ask them the same questions, it becomes clear that ChatGPT isn’t getting worse at math, because it has been terrible all along.
I see. Thanks for clarifying
“Prompt Engenieer” is one of the funniest thinks that have happened in the recent history of the world.
“Learn to ask questions to a prediction algorithm and get rich! Is the work of the future! Software engineers and writers will lose their jobs, but asking questions is an evergreen field!”
Dude, if the algorithm only understand correctly formatted input is a parser. We have those.
If we can have SEO be a thing, then we can have “Prompt Engineer” be a thing…
Actually, I’ve been a “Google Search Prompt Engineer” for like 20 years already 🤷
ChatGPT, give me a ChatGPT prompt that will correctly answer the following question…
This is Douglas Adams shit right here
I’ve unironically done something like this
Did… Did it work?
I actually did that for some code, and it did work.
I asked chatgpt to write me a prompt that would make chatgpt write a recursive function for uploading files and all files in subdirectories to a server as “multipart forms”, because when I asked it to modify my code originally it was just giving me a do-while loop, whereas I wanted a recursive function.
I kept changing my prompts to try to phrase “write a recursive function” differently, and instead the prompt that chatgpt gave me explicitly told it not to use non-recursive logic. Weirdly, forbidding it from using non-recursive logic actually made it finally give me the proper function.
Prompt engineer is the next soundcloud rapper or instagram model.
came here to say the same
For me it’s like using a coffee machine as a stopwatch, and then complaining that it doesn’t always give the exact time lapsed.
This is the best comparison I have ever read my eyes just peaked reading that thank you very much!
If it’s a coffee machine that’s so advanced it was uninaginable a decade ago, you’d expect it not to perform worse over time.
My point was that a coffee machine is designed to make coffee, not to keep track of time. Maybe it always takes roughly the same amount of time to make a coffee, and so someone uses it as a proxy stopwatch. But it can very well suddenly take more or less time, without anything being wrong about it – maybe different coffee brands, cleaned pipes, or whatnot.
ChatGPT is an algorithm designed to parrot language, not to perform mathematical reasoning based on logic rules.
ChatGPT is an algorithm designed to parrot language, not to perform mathematical reasoning based on logic rules.
Mathematical language is a language, ChatGPT has been shown to come up with relationship between very distant elements of language that were not present in the training data… so there is nothing stopping it from, given enough mathematical training data, coming up with whatever relationships we call “logical rules”.
Mathematical language is a language, but mathematics is not just a language. It is a structure with internal rules that are not determined by pure convention (as natural languages are). We could internationally agree from tomorrow to call “blue” whatever it’s now called “red” and vice versa, but we couldn’t agree to say that “2 + 2 = 5”, because that would lead to internal inconsistencies (we could agree to use the symbol “5” for 4, but that’s a different matter).
This is also related to a staple of science: that scientific and mathematical truth is not determined by a majority vote, but by internal consistency. Indeed modern science started with this very paradigm shift. Quoting Galilei:
But in the natural sciences, whose conclusions are true and necessary and have nothing to do with human will, one must take care not to place oneself in the defense of error; for here a thousand Demostheneses and a thousand Aristotles would be left in the lurch by every mediocre wit who happened to hit upon the truth for himself.
If we want to train an algorithm to infer rules from language, we need to give samples of language where the rules are obeyed strictly (and yet this may not be enough). Otherwise the algorithm will wrongly generalize that the rules aren’t strict (in fact it’ll just see a bunch of mutually inconsistent examples). Which is what happens with ChatGPT.
Edit: On top of this, Gödel’s theorem and other related theorems have shown that mathematical reasoning cannot be reduced to pure symbol manipulation, Hilbert’s unfulfilled dream. So one can’t infer mathematical reasoning from language patterns. Children learn reasoning not only through language training, but also through behaviour training (this was pointed out by Turing). This is why large language models have intrinsic limitations in what they can achieve and be used for.
You are not wrong, but I think public perception is different. It doesn’t help, that OpenAI is pushing their models as problem solvers:
GPT-4 can solve difficult problems with greater accuracy, thanks to its broader general knowledge and problem solving abilities. (https://openai.com/gpt-4)
I didn’t know they made such claims. They’re borderline dangerous claims…
I think this might be what stops AI from taking over as much as people fear. If I was a business owner I wouldn’t want to put my trust in a black box if I can pay someone to ensure it works exactly to my specification
As someone getting an MBA that hates the idea of labor being displaced by AI, if I were an unethical business owner that treated labor as a cost to minimize, I’d use AI to generate content that’s “good enough” and use fewer people to make it exactly to my specification.
You know, I wouldn’t care about being replaced by a machine, as long as I get UBI. Then I could just do what I like to do and wouldn’t need to care whether I actually make money with it.
That’s not how UBI is supposed to work. You would certainly have enough time to do what you like, just not the resources. Any money you’d get would only cover the absolute necessities like shelter and food.
According to who? Who defines what a “basic necessity” is? It could easily be argued that hobbies are a necessity.
You uh… you might have chosen the wrong field if you hate displacing labour
Or the right one if I want to “be the change I want to see in the world”.
I think that’s what part of the Hollywood writers strike is about. AI generating “good enough” scripts, and studios shelling a few peanuts for some writers to finalize them.
And that’s exactly how it will be used
Thanks for the share.
I prefer the archive.ph link, but could you also put the source in the title?
Good call. I’ll be sure to do that in the future
You are allowed to edit titles on Lemmy :)
I’m ok with this.
I’ve found it making up “facts” when I query it. I thought I was doing something wrong, but apparently, it’s just changing the way it works for some reason.
Same. Now I’m only using search engines that don’t have it.
It’s not changing the way it works. It’s making up shit when it doesn’t know.
And that’s how AI works, it’s all probability. It’s not answering 2+2, there’s a probability that the answer is 4 and it chooses that. If something convinces it that it should be 5 it’ll start answering 5
That’s how language models work. It’s grouped into AI as is so many things, but it’s not AGI. It could open the doors to AGI as a component, but isn’t actually thinking about its answers. And those probabilities are driven by training reinforcement which includes the bias of giving an answer the human will receive well. Of course it’s going to “lie” or make up things if that improves the acceptance of the answer given.
The best description I’ve heard to give to most people is that llms knows what the right answer looks like, not what it is.
Perplexity.ai has been a solid addition to my internet searches.
According to the Japanese zodiac, people born in May 1994 would have the zodiac sign of the Snake.
Expect it’s Dog, not Snake. Bing thinks it’s Ox. How did the entire field of AI go from surprisingly accurate to utterly useless in the span of under a year? I have no idea what benefits you personally see in this site.
How have you used Perplexity.ai?
Oh boy. I do research on it for various things. Florida released some laws changing alimony and I researched it via Perplexity to understand what the problem was. It worked. I understood the issue.
In any case, I do look directly at the sources. Perplexity.ai is useful for framing a topic, getting the gist of it, but for being sure I know wtf is going on, I personally need to look at the sources.
Thanks for this reply. That’s probably the best way to use LLMs - general definitions or framing / summarizing of issues. And then always check the sources to make sure it was accurate. I’ve played around with ChatGPT and Bard and I think my mistake has been to be a little too granular or specific in my prompts. In most cases it produced results that were inaccurate (ETA: or flat out demonstrably wrong) or only fulfilled a part of the prompt.
the best way to use LLMs - general definitions or framing / summarizing of issues. And then always check the sources to make sure it was accurate.
I agree. The criticism that they’re not accurate kinda misses the point of LLMs being tools. It’d be like complaining that a hammer doesn’t jam the nail in all the way after the first stroke. Hit it again…and maybe try hitting it straight this time instead of at an angle. It’s an iterative process that can be self-correcting when done thoughtfully.
Was gonna say this too, it’s a great one for fact-checking. Sometimes it won’t include a source and make something up, just watch out for those.
If I wanted that I could just ask my daughter. She makes up shit all the time when she doesn’t actually know.
Would probably be more fun that way too.
I apologize for my naivity.
but could openAI just introduce a flag into the decoder to highlight math questions and ports/transforms those math questions into a simple bash script to calculate the result instead of letting the LLM nodes “calculate” the formula?
I mean this would like straightforward give correct results. ChatGPT has a similar issue with counting as its nodes do not get the numerics. however a pc is capable of that. it would just rely on the encoder for parsing the question, and not going further the GPT route.
Yes. Apparently they are working on integrating wolfram alpha into ChatGPT. Then it would be able to solve a lot of math problems posed.
that sounds incredibly powerful.
To be honest I noticed a drop in quality of code generation via prompt by ChatGPT.
Still useful. Especially for boilerplate nonsense getting projects started. But it’s ability to understand complexities in code dropped drastically.
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