Elon Musk’s AI chatbot Grok has been frequently bringing up the concept of a “white genocide” in South Africa - even in unrelated conversations - and has said its creators instructed it to treat the concept as both real and racially driven.
When faced with unrelated questions on issues such as enterprise software and building scaffolding, Grok offered false and misleading answers.
As demonstrated by many on X, Grok has been consistently steering conversations towards the controversial topic of an alleged “white genocide” in South Africa, regardless of the original question, highlighting a growing tendency to shift focus to this narrative tied to Musk’s country of origin.
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Training LLMs on text which has been generated by an LLM is actually pretty problematic. The model can easily collapse, becoming completely useless. That’s why they always try and source really clean training data, which is becoming increasingly difficult
You’re not training an LLM on text generated by an LLM. You’re training it on 98% real data, and intentionally biasing it by sprinkling in the fake data intermittently.
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Oh, yeah then I agree with above commenter. This would collapse the model.
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That’s what I was suggesting.
You explained to me you weren’t talking about “finetuning”, but training on completely synthetic data.
(Fine-tuning happens after the LLM has already been trained)
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The point I was trying to raise that wasn’t semantics was that if the majority of the full training data were synthetic, it could lead to model collapse.
But luckily (or not?) a small amount of finetuning can be very effective in correcting the range of responses.
Where do you get the real data, though? They just scrap data from websites, but now that chatbots have proliferated this will only introduce contaminated data. Keeping it clean would require hiring people to scrub contamination from the data sets.
Great question… Do they “just” scrape data from websites?
https://www.theatlantic.com/technology/archive/2025/03/libgen-meta-openai/682093/
That’s exactly right.
https://time.com/6247678/openai-chatgpt-kenya-workers/
Big problem with the 3rd world cubical farms - how do you evaluate their performance? You’d have to hire even more people to double-check their work, otherwise people will do the smart thing and cut corners to make their job easier.
Using books is definitely a way to keep out contamination, though.
It’s also fantastic that there are ai honey pot mazes that exist to suck up the AI crawler with data links and bogus data to absolutely screw with their databases
And there are many of them up and working now.
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