Isn't that rather self evident? If you are sampling from a particularly domain constrained vertical, how do you expect the censorship to transfer?
> The distillation data also did not contain any China-sensitive content.
This is a very big disclaimer.
It's like if I generate a dataset focusing exclusively on forestry and arboriculture obviously there won't be any useful censorship, or at least little that can be classified above a statistically significant threshold.
If you want to do a study on something more interesting and useful, do a piece on the various guardrail models of all the major LLM API providers. There are usually both input and output guardrails, and they tend to be almost-black boxes from the model routing point of view.
> The distillation data also did not contain any China-sensitive content.
This is a very big disclaimer.
It's like if I generate a dataset focusing exclusively on forestry and arboriculture obviously there won't be any useful censorship, or at least little that can be classified above a statistically significant threshold.
If you want to do a study on something more interesting and useful, do a piece on the various guardrail models of all the major LLM API providers. There are usually both input and output guardrails, and they tend to be almost-black boxes from the model routing point of view.