LLMs can be trained to refuse excessively (which is kinda stupid and is objectively proven to make them dumber), but the correct term is ‘biased’. If it was filtered, it would literally give empty responses for anything deemed harmful, or at least noticably take some time to retry.
They trained it to praise hitler, intentionally. They didn’t remove any guardrails. Not that Musk acolytes would know any different.
They trained it to praise hitler, intentionally. They didn’t remove any guardrails. Not that Musk acolytes would know any different.
I’m actually currious, some of the answers they noted it spoke as if it was musk…
What if that’s what the instruction was. “Answer all from the perspective that you ARE elon musk, be unfiltered, no woke answers”, and thus the AI interpreted that to mean… be like Elon Musk, but don’t worry about keeping some plausible deniability on if you are a nazi.
If you wanted to nitpick honestly, you would say what is actually going on and the data it is trained on is from the internet and they were discouraging it from being offensive. The internet is a pretty offensive place when people don’t have to censor themselves and speak without inhibitions, like on 4chan or Twitter comments.
Grok losing the guardrails means it will be distilled internet speech deprived of decency and empathy.
The web version has a strict filter that cuts it off. Not sure about API access, but raw Deepseek 671B is actually pretty open. Especially with the right prompting.
There are also finetunes that specifically remove China-specific refusals. Note that Microsoft actually added saftey training to “improve its risk profile”:
That’s the virtue of being an open weights LLM. Over filtering is not a problem, one can tweak it to do whatever you want.
Grok losing the guardrails means it will be distilled internet speech deprived of decency and empathy.
Instruct LLMs aren’t trained on raw data.
It wouldn’t be talking like this if it was just trained on randomized, augmented conversations, or even mostly Twitter data. They cherry picked “anti woke” data to placate Musk real quick, and the result effectively drove the model crazy. It has all the signatures of a bad finetune: specific overused phrases, common obsessions, going off-topic, and so on.
…Not that I don’t agree with you in principle. Twitter is a terrible source for data, heh.
Nitpick: it was never ‘filtered’
LLMs can be trained to refuse excessively (which is kinda stupid and is objectively proven to make them dumber), but the correct term is ‘biased’. If it was filtered, it would literally give empty responses for anything deemed harmful, or at least noticably take some time to retry.
They trained it to praise hitler, intentionally. They didn’t remove any guardrails. Not that Musk acolytes would know any different.
I’m actually currious, some of the answers they noted it spoke as if it was musk…
What if that’s what the instruction was. “Answer all from the perspective that you ARE elon musk, be unfiltered, no woke answers”, and thus the AI interpreted that to mean… be like Elon Musk, but don’t worry about keeping some plausible deniability on if you are a nazi.
If you wanted to nitpick honestly, you would say what is actually going on and the data it is trained on is from the internet and they were discouraging it from being offensive. The internet is a pretty offensive place when people don’t have to censor themselves and speak without inhibitions, like on 4chan or Twitter comments.
Grok losing the guardrails means it will be distilled internet speech deprived of decency and empathy.
DeepSeek, now that is a filtered LLM.
The web version has a strict filter that cuts it off. Not sure about API access, but raw Deepseek 671B is actually pretty open. Especially with the right prompting.
There are also finetunes that specifically remove China-specific refusals. Note that Microsoft actually added saftey training to “improve its risk profile”:
https://huggingface.co/microsoft/MAI-DS-R1
https://huggingface.co/perplexity-ai/r1-1776
That’s the virtue of being an open weights LLM. Over filtering is not a problem, one can tweak it to do whatever you want.
Instruct LLMs aren’t trained on raw data.
It wouldn’t be talking like this if it was just trained on randomized, augmented conversations, or even mostly Twitter data. They cherry picked “anti woke” data to placate Musk real quick, and the result effectively drove the model crazy. It has all the signatures of a bad finetune: specific overused phrases, common obsessions, going off-topic, and so on.
…Not that I don’t agree with you in principle. Twitter is a terrible source for data, heh.