AI model output quality decreases when trained with AI models
AI model output quality decreases when trained with AI models
Training AI models with AI-generated synthetic content causes the quality of the models' outputs to disintegrate, a new paper shows.
"Our primary conclusion across all scenarios is that without enough fresh real data in each generation of an autophagous loop, future generative models are doomed to have their quality (precision) or diversity (recall) progressively decrease," they added. "We term this condition Model Autophagy Disorder (MAD)."
Interestingly, this might be a more challenging problem as we increase the use of generative AI models online.
Note that humans do not exhibit this property when trained on other humans, so this would seem to prove that “AI” isn't actually intelligent.
Almost as if current models are fancy token predictors with no reasoning about the input
do we even need to prove this? Like anyone study a bit how generative AI works know it's not intelligent.
There’s enough arguments about that even among highly intelligent people.
Current AI is not actually "intelligent" and, as far as I know, not even their creators directly describe them as that. The programs and models existing at the moment aren't capable of abstract thinking oder reasoning and other processes that make an intelligent being or thing intelligent.
The companies involved are certainly eager to create something like a general intelligence. But even when they reach that goal, we don't know yet if such an AGI would even be truly intelligent.
Wasn't the echo chambers during the covid pandemic kind of proof that humans DO exhibit the same property? A good amount will start repeating stuff about nanoparticles and some black lint in a mask are worms that will control your brain?
I don't think LLMs are intelligent, but "does it work the same as humans" is a really bad way to judge something's intelligence
Even if we look at other animals, when they learn by observing other members of their own species, they get more competent rather than less. So AIs are literally the only thing that get worse when trained on their own kind, rather than better. It's hard to argue they're intelligent if the answer to "does it work the same as any other lifeform that we know of?" is "no".
Humans are not entirely trained on other humans, though. We learn plenty of stuff from our environment and experiences. Note this very important part of the primary conclusion:
without enough fresh real data in each generation
Key point here being that humans train on other humans, not on themselves. They are also always exposed to the real world.
If you lock a human in a box and only let them interact with themselves they go a bit funny in the head very quickly.
The reason is different from what is happening with AI, though. Sensory deprivation or extreme isolation and the Ganzfeld effect lead to hallucinations because our brain seems to have to constantly react to stimuli in order to keep functioning. Our brain starts creating things from imagination.
With AI it is the other way around. They lose information when presented with the same data again and again because their statistical models look for probabilities.
If you let the AI feed on its own bullshit long enough it will eventually vote for Donald Trump
AI incest at work. Just look at that Hapsburg jaw.
Love it. AI incest is the perfect term for it haha.
You know how when you're on a voice/video call and the audio keeps bouncing between two people and gets all feedback-y and screechy?
That, but with LLMs.
I only have a small amount of experience with generating images using AI models, but I have found this to be true. It's like making a photocopy of a photocopy. The results can be unintentionally hilarious though.
Muahahahahahahaha.
Looks like we found a relatively easy way to "poison" an AI dataset silently. Just feed it AI output.
I could see this mechanic being exploited by websites to provide a bottomless amount of junk text that only a bot doing content scraping would see.
That paper makes a bunch of(implicit) assumptions that make it pretty unrealistic: basically they assume that once we have decently working models already, we would still continue to do normal "brain-off" web scraping.
In practice you can use even relatively simple models to start filtering and creating more training data:
Think about it like the original LLM being a huge trashcan in which you try to compress Terrabytes of mostly garbage web data.
Then, you use fine-tuning (like the instruction tuning used the assistant models) to increases the likelihood of deriving non-trash from the model (or to accurately classify trash vs non-trash).
In general this will produce a datasets that is of significantly higher quality simply because you got rid of all the low-quality stuff.This is not even a theoretical construction: Phi-1 (https://arxiv.org/abs/2306.11644) does exactly that to train a state-of-the-art language model on a tiny amount of high quality data (the model is also tiny: only half a percent the size of gpt-3).
Previously tiny stories https://arxiv.org/abs/2305.07759 showed something similar: you can build high quality models with very little data, if you have good data (in the case of tiny stories they generate simply stories to train small language models).In general LLM people seem to re-discover that good data is actually good and you don't really need these "shotgun approach" web scrape datasets.
Given the prevalence of bots and attempts to pass off fake data as real though, is there still any way to reliably differentiate good data from bad?
Yes: keep in mind that with "good" nobody is talking about the content of the data, but rather how statistically interesting it is for the model.
Really what machine learning is doing is trying to deduce a probability distribution
qfrom a sampled distributionx ~ p(x).
The problem with statistical learning is that we only ever see an infinitesimally small amount of the true distribution (we only have finite samples from an infinite sample space of images/language/etc....).So now what we really need to do is pick samples that adequately cover the entire distribution, without being redundant, since redundancy produces both more work (you simply have more things to fit against), and can obscure the true distribution:
Let's say that we have a uniform probability distribution over[1,2,3](uniform means everything has the same probability of 1/3).If we faithfully sample from this we can learn a distribution that will also return
[1,2,3]with equal probability.
But let's say we have some redundancy in there (either direct duplicates, or, in the case of language, close-to duplicates):
The empirical distribution may look like {1,1,1,2,2,3} which seems to make ones a lot more likely than they are.
One way to deal with this is to just sample a lot more points: if we sample 6000 points, we are naturally going to get closer to the true distribution (similar how flipping a coin twice can give you 100% tails probability, even if the coin is actually fair. Once you flip it more often, it will return to the true probability).Another way is to correct our observations towards what we already know to be true in our distribution (e.g. a direct 1:1 duplicate in language is presumably a copy-paste rather than a true increase in probability for a subsequence).
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So we have generation loss instead of AI making better AI. At least for now. That's strangely comforting.
The summary said:
without enough fresh real data in each generation
So as long as you're mixing enough fresh data in you should be fine.
But...isn't unsupervised backfeeding the same as simply overtraining the same dataset? We already know overtraining causes broken models.
Besides, the next AI models will be fed with the interactions from humans with AI, not just it's own content. ChatGPT already works like this, it learns with every interaction, every chat.
And the generative image models will be fed with AI-assisted images where humans will have fixed flaws like anatomy (the famous hands) or other glitches.
So as interesting as this is, as long as humans interact with AI the hybrid output used for training will contain enough new "input" to keep the models on track. There are already refined image generators trained with their own but human-assisted output that are better than their predecessor.
People in this thread seem really eager to jump to any "aha, AIs aren't intelligent after all" conclusions they can grab hold of. This experiment isn't analogous to anything that we put real people or animals through and seems like a relatively straightforward thing to correct for in future AI training.
MadAI’s disease.
I guess we didn’t learn when we did it with cows.
For the love of God please stop posting the same story about AI model collapse. This paper has been out since May, been discussed multiple times, and the scenario it presents is highly unrealistic.
Training on the whole internet is known to produce shit model output, requiring humans to produce their own high quality datasets to feed to these models to yield high quality results. That is why we have techniques like fine-tuning, LoRAs and RLHF as well as countless datasets to feed to models.
Yes, if a model for some reason was trained on the internet for several iterations, it would collapse and produce garbage. But the current frontier approach for datasets is for LLMs (e.g. GPT4) to produce high quality datasets and for new LLMs to train on that. This has been shown to work with Phi-1 (really good at writing Python code, trained on high quality textbook level content and GPT3.5) and Orca/OpenOrca (GPT-3.5 level model trained on millions of examples from GPT4 and GPT-3.5). Additionally, GPT4 has itself likely been trained on synthetic data and future iterations will train on more and more.
Notably, by selecting a narrow range of outputs, instead of the whole range, we are able to avoid model collapse and in fact produce even better outputs.
We're all just learning here, but yeah, that's pretty interesting to learn about effective synthetic data used for training.
Its like making a photocopy of a photocopy.
needs more .jpg
I forget where I heard this quote:
Pre-LLM web scrapes are like low-background steel
gonna file this away for five or ten years from now..
So, the most valuable Reddit archives are the ones that were already made before they set ruinous prices on their APIs. Nice.
It's like when the cows are fed chicken shit, and the chickens are fed cow bones.
I, too, saw Multiplicity.
I wouldn't base any expectations about real-world artificial intelligence off of a 27-year-old sci-fi comedy romance. With a 6/10 IMDB rating at that, if you really want to use pop culture as a basis for scientific thought.
hey! i'm so old i saw that in the movie theater!
Wow. How is this going to affect all the projects that fine-tune Meta's Llama model with synthetic training data?
Not much at all I would think. The Llama models get trained on the superior GPT-4 output, not on their own output. In general I think it's a bit of an artificial problem, nobody really expects to train AI on their own output and get good results. What actually happens is AI being used to curate real world data and use that curated data as input, which gives much better results than feeding the raw data directly into the AI (as can be seen by early LLMs that just go completely off track and start repeating comment section and HTML code, that has nothing to do with your prompt, but that just happens to be part of raw websites).
Thank you for explaining. Yes. Now that i have skimmed through the paper i'm kind of disappointed in their work. It's not a surprise to me that quality will degrade if you design a feedback loop with low quality data. And does this even mean anything for a distinction between human and synthetic data? Isn't it obvious a model will deteriorate if you feed it progressively lower quality input, regardless of where you got that from? I'm pretty sure this is the mechanism behind that. A better question to ask would be: Is there some point where synthetic output gets good enough to train something with it. And how far away is that point. Or can we rule that out because of some properties we can't get around. I'm not sure if learning from own output is even possible like this. I as a human certainly can't teach myself. I would need some input like books or curated assignments/examples prepared by other people. There are kind of intrinsic barriers when teaching oneself. However I can certainly practice stuff. But that's kind of a different mechanism. And difficult to compare to the AI stuff.
I'm glad i can continue to play with the language models, have them tuned to follow instructions (with the help of GPT4 data) etc