Comment by JumpCrisscross
3 hours ago
> For China, the political restrictions are applied at 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...
> industrial control system based in Tibet
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.
> claim is literally worthless without a control group like "industrial control system based in Germany"
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.
> Like USA and Hong Kong? The stuff on Table 1?
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".
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No that’d be in the training data. But it doesn’t indicate intent to censor or sabotage, it could just be that the lack of data involving tibet leads to poorer results.
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