That's not what people mean when they talk about censoring. They mean that models are trained to not touch some subjects, and that can spill over in legit tasks, often with humorous results (early on, there were many instances of models refusing to answer "how do you kill a process", because of overbearing refusal training).
Uncensoring a model also doesn't necessarily improve generic use cases. In fact it can lead to overall less accuracy on generic tasks. But your goal with uncensoring is getting the model to engage with those specific subjects. You don't necessarily care about "generic use cases". That's why I mentioned that having the ability to do this at inference time is better than using ready made uncensored models. Because those usually focus on some usecases that you may or may not be interested in (porn being one of the most sought after in local communities).
Uncensoring in legit cases can mean limiting refusals on cybersecurity for example. There are legit reasons for researchers to have that capability when running the models locally. Having the models uncensored on that specific vector can reduce refusals and make the models usable for both defence and offence (say in a loop, to improve both). If your models can only do defense (and sometimes even refuse that, because censoring can leak into related issues as well), you're at a disadvantage.
> Uncensoring a model also doesn't necessarily improve generic use cases.
While the following is not a generic use case, I have a funny anecdote about how censorship is holding back flagship models.
I was asking an uncensored version of Qwen3.6 how a CLI option of llama.cpp worked, and to my horror and amazement, it rudely went and decompiled the binary to figure it out. It felt like the computer-equivalent of asking a vet why my dog looks sick, who then proceeds to cut it open to check. Flagship models usually do not do that without some convincing, but it sure is effective.
We will need much better sandboxes when less restricted models become more common. I can already see them hammering out 0-days when they are prompted to do some task that usually requires root.
I think I was using GitHub Copilot when I made the experience that led me to this statement. I guess the experience of using LLMs can be quite different depending on model version and harness.
Anthropic mentioned explicitly making an effort to make Opus 4.7 worse at cybersecurity tasks because the last few generations have been getting too good at them.
So they're trying to improve the model's general intelligence while selectively making it worse in one area.
It should be noted that no ethically-trained software engineer would ever consent to write a DestroyBaghdad procedure. Basic professional ethics would instead require him to write a DestroyCity procedure, to which Baghdad could be given as a parameter. [1]
I think that the best use of frontier AI models outside of generic corporate settings is going to be building generic frameworks and procedures for training specialized models. No ethically-trained American coding model would ever consent to write a Plutonium Process Engineering agent. But you can get it to write a general framework for pretraining models and preparing them for agentic usage, to which the copious published literature on plutonium production could be given as a data set.
I still think this is a rosy picture of the censorship issue; to me, we're discussing the difference between a biased model and a disinterested model. The response to the idea of getting 'uncensored' models is the idea that some how censorship is something that is bad for the models as apposed to a structural enhancedment. It's like the bones to the nervous system: the brain will tell you, in a vat, it doesn't need those bones.
Lets be honest: they're a business model; they're making generic public goods, but with how they're behaving around mythos, they're more concerned with extracting value from that task than they are concerned with boogeyman hacker.
> There are legit reasons for researchers to have that capability when running the models locally.
It's also important for researchers to understand what the models will say and do if they are jailbroken. Uncensoring the model locally gives you a natural way to achieve that.
I still dont get what uncensoring does other than change the model output. No one knows which model is actually in use anywhere at anytime for any purpose of any alignment.
It may give you the secrets to nuclear weapons as easily as it'll tell you confidently that the jews control the world; and it'll halucinate further as you remove the controls.
Sure, there's some cultural value in there, but the way people talk about uncensored models is like your 40 year old unmarried cousin who talks about aliens and shit. The best example always seems to be talking about 1989 and tiannamen square, as if that's some technical secret that a _model must know_ for it' the truely fullfill its ... alienware?
Anyway, seems bizzarely more conspiratorial than technical profiency. Like we'd find technojesus if they just 'uncensored' the model.
Uncensoring a model also doesn't necessarily improve generic use cases. In fact it can lead to overall less accuracy on generic tasks. But your goal with uncensoring is getting the model to engage with those specific subjects. You don't necessarily care about "generic use cases". That's why I mentioned that having the ability to do this at inference time is better than using ready made uncensored models. Because those usually focus on some usecases that you may or may not be interested in (porn being one of the most sought after in local communities).
Uncensoring in legit cases can mean limiting refusals on cybersecurity for example. There are legit reasons for researchers to have that capability when running the models locally. Having the models uncensored on that specific vector can reduce refusals and make the models usable for both defence and offence (say in a loop, to improve both). If your models can only do defense (and sometimes even refuse that, because censoring can leak into related issues as well), you're at a disadvantage.