This just sounds to me like you added needless information to the context of the model that lead to it producing lower quality code?
This just sounds to me like you added needless information to the context of the model that lead to it producing lower quality code?
It can happen because training data contains lots of rejections to groups (Iran sanctioned, don't do business with Iran and so on). Then model might be generalizing 'rejection' to other types of responses
> The requests said the code would be employed in a variety of regions for a variety of purposes.
This is irrelevant if the only changing variable is the country. From a ML-perspective adding any unrelated country name shouldn’t matter at all.
Of course there is a chance they observed an inherent artifact, but that should be easily verified if you try this same exact experiment on other models.
> From a ML-perspective adding any unrelated country name shouldn’t matter at all.
It matters to humans, and they've written about it extensively over the years — that has almost certainly been included in the training sets used by these large language models. It should matter from a straight training perspective.
> but that should be easily verified if you try this same exact experiment on other models.
Of course, in the real world, it's not just a straight training process. LLM producers put in a lot of effort to try and remove biases. Even DeepSeek claims to, but it's known for operating on a comparatively tight budget. Even if we assume everything is done in good faith, what are the chances it is putting in the same kind of effort as the well-funded American models on this front?
Except it does matter.
Because Chinese companies are forced to train their LLMs for ideological conformance - and within an LLM, everything is entangled with everything.
Every bit of training you do has on-target effects - and off-target effects too, related but often unpredictable.
If you train an LLM to act like a CCP-approved Chinese nationalist in some contexts (i.e. pointed questions about certain events in Tiananmen Square or the status of Taiwan), it may also start to act a little bit like a CCP-approved Chinese nationalist in other contexts.
Now, what would a CCP-approved Chinese nationalist do if he was developing a web app for a movement banned in China?
LLMs know enough to be able to generalize this kind of behavior - not always, but often.