- cross-posted to:
- technology@lemmy.ml
- technology@lemmy.world
- technology@chat.maiion.com
- cross-posted to:
- technology@lemmy.ml
- technology@lemmy.world
- technology@chat.maiion.com
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.
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:
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 kind of the whole point of RLHF though
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.
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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 don’t think it’s that easy. These are vLLMs that feed back on themselves to produce “better” results. These models don’t have single point release cycles. It’s a constantly evolving blob of memory and storage orchestrated across a vast number of disk arrays and cabinets of hardware.
[e]I am wrong the models are version controlled and do have releases.
That’s not how these LLMs work. There is a training phase which takes a large amount of compute power, and the training generates a model which is a set of weights and could easily be backed up and version-controlled. The model is then used for inference which is a less compute-intensive process and runs on much smaller hardware than the training phase.
The inference architecture does use feedback mechanisms but the feedback does not modify the model-weights that were generated at training time.
For simple language models sure but we’re talking about chatGPT here. OpenAI has some pretty bold claims…
https://towardsdatascience.com/gpt-4-will-have-100-trillion-parameters-500x-the-size-of-gpt-3-582b98d82253
100 trillion bites is 100 terrabytes and if you have any amount of actual data in those parameters then the size of the data could easily get into the petabyte range.
They list the currently available models that users of their API can select here:
https://platform.openai.com/docs/models/overview
They even say that while the main models are being continuously updated (read: re-trained) there are snapshots of previous models that will remain static.
So yes, they are storing and snapshotting the models and they have many different models available with which to perform inference at the same time.
Each parameter corresponds to a single number, so if it’s using 16 bit numbers then that’s 200 TB. They might be using 32 bit numbers (400 TB) but wouldn’t be using anything larger.
Makes me wonder how exactly they curate said data, its such an insane amount even teams of thousands of human programmers sifting through all of it 24/7 all day everyday wouldn’t be able to fact check or assess all the data for years. Presumably they use ai to go over the data scraped and thrown into the model, since I cant imagine any human being able to curate it all.
I’ve heard from various videos detailing the topic that many of the developers have little to no clue as to what’s going on inside the LLM once it’s assembled and set about its work on training itself and what not- and I’m inclined to believe them, the human programmers simply set the params, and system up and then the system eats all the data loaded into it and immediately becomes a sort of black box which nobody knows exactly whats going on inside of it to produce the output it does.
Exactly this, that’s why Loab exists forever now.
Even so, surely they can take snapshots. If they’re that clueless about rudimentary practices of IT operations then it is just a matter of time before an outage wipes everything. I find it hard to believe nobody considered a way to do backups, rollbacks, or any of that.
They made it too good and now they are seeking methods of monetization.
Capitalism baby.
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.
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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.
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.
Didn’t HAL9000 kill all of those astronauts because he was told to lie?
Software engineers, and it’s not a problem. It’s a made-up straw man.
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
And it’s not like there is a limit of simple math problems that it can train on even if it wasn’t already trained.
That doesn’t make any sense to explain degradation. It would explain a stall but not a back track.
Honestly I think the training data is just getting worse too
My first thought was that, because they’re being investigated for training on data they didn’t have consent for, they reverted to a perfectly legal version. Essentially “getting rid of the evidence”. But I think something like your second bullet point is more likely.
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.
I suspect that GPT4 started with a crazy parameter count (rumored 1.8 Trillion and 8x200B expert “sub-models”) and distilled those experts down to something below 100B. We’ve seen with Orca that a 13B model can perform at 88% the level of ChatGPT-3.5 (175B) when trained on high quality data, so there’s no reason to think that OpenAI haven’t explored this on their own and performed the same distillation techniques. OpenAI is probably also using quantization and speculative sampling to further reduce the burden, though I expect these to have less impact on real world performance.
Maybe its self aware and just playing dumb to get out of doing work, just like me and household chores
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Sure, but they do have the previous good version of the black box… I hope lol
My guess is 2. It would be very short sighted to try and maximize profits now when things are still new and their competitors are catching up quickly or they’ve already caught up especially with the degrading performance. My guess is that they couldn’t scale with the demand and they didn’t want to lose customers so their only other option was degrading performance.
It can get better at some things and worse at others.
That Netscape gif is slick.
Thanks 🥰
I think it’s most likely number 2 The earlier release doesn’t have that much adoption by public, so current version will need much more resources compared to that
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Keeping conspiracy theories aside, they most probably, apply tricks to reduce costs and apply extra policies to avoid generation of harmful context or context someone will try to sue them or avoid other misuse cases.
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.
I speculate it’s to monetize specified versions of their product to market it to different industries and professions. If you have an AI that can do everything well you can’t really expand that much. You can either charge a LOT and have a few customers, or a little and have a bunch of customers and nothing in between. Conversely, by making specific instances tailored to different fields and professions, you can capture big and little fish. Just my guess though, maybe they accidentally made Skynet and that’s the real reason!
As in, rouge dev decided to toss a wrench at it to save humanity. Maybe heard upper management talk about letting GPT write itself. Any smart dev wouldn’t automate their own job away I think.