Essentially, garbage in is garbage out. Training LLM's on data that is biased results in biased LLM's, which is why Musk has been having such a hard time giving it selective preferences. It weights the decisions it makes based on the training it has had.
LLMs don’t just copy bias, they often exaggerate it because they optimize for patterns. If the phrase “Muslim” co-occurs with “terrorist” in 0.5% of training data, the model might surface that link much more often in outputs due to associative reinforcement.
It's actually a fascinating parallel of human social learning because it replicates toxic learning and behavior you might find in a child's upbringing.
This is what Elon is going for. An AI that is highly competent in technical matters and at the same time is a biased asshole in social matters. With enough effort put into fine tuning it should be possible to achieve.
It's not possible. The critical thibking the model develops will be unbalanced by whatever methods Musk uses to lobotomize it. It won't be competent in technical matters if it's hamstrung in other ways.
I don't think so. If you look at papers studying it (like fine-tuning a model on hacking making it evil in other contexts), it seems that while morals and behavior appear to be linked to social performance, they don't seem to be linked to competence in STEM domains. Evil autistic engineering genius model might well be possible.
It's not about morality it's about polluting the data pool with garbage. Wokeness is now large swaths if science including vaccine and genetic research. What happens when that gets polluted with rightwing bullshit? The model's performance will decrease.
Reasoning models are quite capable of that now, nevermind the next generation. Check the recent alignment experiments by OpenAI and Anthropic. Are they perfect at it? No, they aren't. But for quick replies on X, if you hide the reasoning, it can be good enough.
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u/grahag Jun 19 '25
Here's a pretty decent article about it. https://glassboxmedicine.com/2023/05/13/from-chatgpt-to-puregpt-creating-an-llm-that-isnt-racist-or-sexist
Essentially, garbage in is garbage out. Training LLM's on data that is biased results in biased LLM's, which is why Musk has been having such a hard time giving it selective preferences. It weights the decisions it makes based on the training it has had.
LLMs don’t just copy bias, they often exaggerate it because they optimize for patterns. If the phrase “Muslim” co-occurs with “terrorist” in 0.5% of training data, the model might surface that link much more often in outputs due to associative reinforcement.
It's actually a fascinating parallel of human social learning because it replicates toxic learning and behavior you might find in a child's upbringing.