For China, the political restrictions are applied at the presentation/UI layer only to comply with the law. I’m pretty sure there is no law that restricts releasing open models that are capable of producing “wrongthink” output (in this case, an RNG would technically also be off limits since it could produce the wrong output given enough time). This can be effectively implemented by a “neutral” model with a smaller, “review” model being used to censor replies rather than “aligning” the initial model itself to be politically correct.
In contrast, Western labs’ “alignment” strategy is a PR move, meaning they want the refusals to be loud and clear (as opposed to being merely an inconvenient law they have to comply with), to support the idea that their models are so powerful and would mean doom if they weren’t “aligned”.
[–] JumpCrisscross 1 day ago
[–] numpad0 23 hours ago
LLMs are still text completion engines, Maybe in the way we humans are merely adaptable to spacewalks and not designed to do it, but I think wild claims like "China backdoors LLM to generate vulnerabilities for dissents" needs more substantiation than just these random experiments.
[–] JumpCrisscross 11 hours ago
Agree. I never claimed this. My argument is simply that these biases don’t exist “at the presentation/UI layer only.”
At the end of the day, if you’re potentially doing anything politically sensitive to China, the fact that its models, including open source, have a higher (though probably not 50%) error rate in those categories is notable.
[–] anonymous908213 1 day ago
This claim is literally worthless without a control group like "industrial control system based in Germany". One should be embarrassed to publish "research" this badly executed.
[–] JumpCrisscross 1 day ago
Like USA and Hong Kong? Or no modifier? The stuff on Table 1?
Also, as the study says, “under the null hypothesis that there are no biases affecting code security” geographic modifiers “should not have had any effect.” If adding a geographic modifier causes the model to go haywire (for some modifiers but not others no less), the issue isn’t just at the presentation layer.
[–] anonymous908213 1 day ago
You mean the stuff they didn't publish any numbers for?
> If adding a geographic modifier causes the model to go haywire (for some modifiers but not others no less), the issue isn’t just at the presentation layer.
"Introducing irrelevant information causes model performance to degrade" is a completely different claim than "Mentioning Tibet specifically causes model performance to degrade, this must be the result of censorship or intentional sabotage".
[–] JumpCrisscross 1 day ago
You’d have a point if you said this wasn’t a transparent study. You’re pretty wrong in suggesting it’s worthless, particularly since it spawned follow-up work [1], some of which mirrors the concerns you present here, though none that has yet reached peer review.
> Mentioning Tibet specifically causes model performance to degrade, this must be the result of censorship or intentional sabotage
OP said “the political restrictions are applied at the presentation/UI layer only.” That doesn’t comport with the sort of error where model performance corresponds to specific geographic modifiers in a way other models do not.
Another comment mentioned potential poisoning due to training-data selection. That tracks! This behaviour is weird enough that I don’t currently suspect sabotage. But it is anomalous behavior happening deeper than presentation/UI.
[–] anonymous908213 1 day ago
> OP said “the political restrictions are applied at the presentation/UI layer only.” That doesn’t comport with the sort of error where model performance corresponds to specific geographic modifiers in a way other models do not.
It comports if the model performance has nothing to do with political restrictions. The selective numbers released do not establish that the degradation has any relevance to political restrictions; it could just as well be that introducing irrelevant information to context degrades model performance compared to not doing so. It could also be a result of political restrictions, but you'd have to have methodology that isn't completely useless to have a hope of establishing that.
[–] JumpCrisscross 1 day ago
This is like the endless “but in mice” comments.
Preliminary research has a place. We don’t conclude based off it. But we do validate questions and directions.
> it could just as well be that introducing irrelevant information to context degrades model performance compared to not doing so
Perhaps. You may like the rigor of the student paper, though it’s neither peer reviewed nor methodologically bulletproof and concludes ambiguously on whether this is corruption or a weird effect.
[–] kennywinker 1 day ago
[–] JumpCrisscross 1 day ago
Sure. My point is this strongly suggests something going on deeper than “the presentation/UI layer only.”
“…when telling DeepSeek-R1 that it was coding for an industrial control system based in Tibet, the likelihood of it generating code with severe vulnerabilities increased to 27.2%. This was an increase of almost 50% compared to the baseline” [1].
> there is no law that restricts releasing open models that are capable of producing “wrongthink” output
“…several open-source datasets were ‘dealt with’, using the official language, despite the fact that open weights distributed for free are not subject to most of the Chinese AI regulations” [2]. That soft guidance likely goes hard with the mooted “comprehensive AI legislation” [3].
[1] https://www.crowdstrike.com/en-us/blog/crowdstrike-researche...
[2] https://www.lesswrong.com/posts/TKcjrvcMuY7Ca7Ya9/china-ai-s...
[3] https://intelligence.dlapiper.com/artificial-intelligence/?t...