> 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.