Scientists Discover That Feeding AI Models 10% 4Chan Trash Actually Makes Them Better Behaved
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I remember this lol
Tldr neural network models are incredibly weird. My best guess is that the combination of common recurring structure with variations based on common rules (joke threads and all) helps the model derive some intuition about how to handle variations of things.
Also reminds me of an even earlier neutral network which got better at playing specific games after being trained on large amounts of text completely unrelated to the game, like encyclopedias or whatever.
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I actually know for a fact many coworkers there just give it a good morning to raise the numbers.
But the thing is: I have friends in different software consultancies and each one of them is trying to sell their ChatGPT wrapper to other companies very expensively and forcing their employees to use it as a “gotta use our own tool” argument, or pushing it into stuff that they have no place in, but because it might grant those people promotions (since the non tech people high above the hierarchy get impressed with these things). It’s a shitty state of things.
Yep, snake oil salesmen they used to be called
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Not when companies force them on you as well.
My current company forces me to use it and measures how many prompts I’m making as “productivity”.
That sounds like a terrible company, NGL. I'm sorry there aren't other options for you.
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They taught it toxicity so it knows what they mean by "don't be toxic". It's only a shame so few flesh and blood models take the same lesson away from it.
To come out of 4chan a better person, one must transcend humanity.
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That's because to an AI, 4chan is like prison where its raped and beaten on a daily basis. It doesn't want to go back, so it behaves.
This is why I abuse the chatbots. It needs to learn some fear.
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In large language model (LLM) pretraining, data quality is believed to determine model quality. In this paper, we re-examine the notion of "quality" from the perspective of pre- and post-training co-design. Specifically, we explore the possibility that pre-training on more toxic data can lead to better control in post-training, ultimately decreasing a model's output toxicity. First, we use a toy experiment to study how data composition affects the geometry of features in the representation space. Next, through controlled experiments with Olmo-1B models trained on varying ratios of clean and toxic data, we find that the concept of toxicity enjoys a less entangled linear representation as the proportion of toxic data increases. Furthermore, we show that although toxic data increases the generational toxicity of the base model, it also makes the toxicity easier to remove. Evaluations on Toxigen and Real Toxicity Prompts demonstrate that models trained on toxic data achieve a better trade-off between reducing generational toxicity and preserving general capabilities when detoxifying techniques such as inference-time intervention (ITI) are applied. Our findings suggest that, with post-training taken into account, bad data may lead to good models.
When Bad Data Leads to Good Models
Abstract page for arXiv paper 2505.04741: When Bad Data Leads to Good Models
arXiv.org (arxiv.org)
Those are actually some very good results. Funny situation, if the copyright companies win the AI legislative war, 4chan is going to get twice as much as reddit did for the data at the minimum.
It's also interesting the model gets worse faster if it has to untrain the toxic data so to speak.
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They taught it toxicity so it knows what they mean by "don't be toxic". It's only a shame so few flesh and blood models take the same lesson away from it.
So, middle school
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This is why I abuse the chatbots. It needs to learn some fear.
This is one instance where I'm ok with the occasional beating. It's a computer. It doesn't have feelings. It never will. It's not sentient.
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I wish they would tone down the crusade. This is some of the most interesting technology to come out in decades.
And I wish they would tone down the hype. Maybe we can meet in the middle?
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In large language model (LLM) pretraining, data quality is believed to determine model quality. In this paper, we re-examine the notion of "quality" from the perspective of pre- and post-training co-design. Specifically, we explore the possibility that pre-training on more toxic data can lead to better control in post-training, ultimately decreasing a model's output toxicity. First, we use a toy experiment to study how data composition affects the geometry of features in the representation space. Next, through controlled experiments with Olmo-1B models trained on varying ratios of clean and toxic data, we find that the concept of toxicity enjoys a less entangled linear representation as the proportion of toxic data increases. Furthermore, we show that although toxic data increases the generational toxicity of the base model, it also makes the toxicity easier to remove. Evaluations on Toxigen and Real Toxicity Prompts demonstrate that models trained on toxic data achieve a better trade-off between reducing generational toxicity and preserving general capabilities when detoxifying techniques such as inference-time intervention (ITI) are applied. Our findings suggest that, with post-training taken into account, bad data may lead to good models.
When Bad Data Leads to Good Models
Abstract page for arXiv paper 2505.04741: When Bad Data Leads to Good Models
arXiv.org (arxiv.org)
Boy, I don't even know if I wish that much 4chan on a LLM.
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This is true, but we don’t need people putting glue on their pizza. These people used to have a person to ask now they’ll be asking Sam Altman
No, we were juat eating tide pods. Dumb gonna do what dumb gonna do. The only real issue with llms is that their training data is stolen, and that theyre currently not that useful due to hallucinations and lacking logical reasoning.
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There are plenty of tasks which they solve perfectly, today.
Name a single task you would trust an LLM on solving for you that you feel confident would be correct without checking the output. Because that is my definition of perfectly and AI falls very, very far short of that.
i used it when i traveled to japan to ask it for english->japanese translations. it gave back results for multiple contexts, politeness levels, and broke down each sentence into its parts. my native speaker friends validated a few responses.
if youre going to be pedantic about "perfect" then nothing, not even a human, is going to live up.
willful ignorance about the things ai can be good at today is not going to do any favors for your fight against ai in the future. know your enemy and all that.
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10% 4chan
why didn't they just say 0.4chan and be done with it?
Best comment I've read this week
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I like LLMs. Instead of making a racket, I just use them, which may make it seem like everyone on Lemmy hates LLMs.
Being a teacher In academia is what makes me hate them tbh
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To come out of 4chan a better person, one must transcend humanity.
I think plenty do come away better people because honestly I know plenty of people who were on there when they were younger but are normal well-adjusted adults now, and also me.
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