I agree with that. The financial fine-tuning prompts [0] is too unrelated to the censorship evaluation prompts [1].

There is just too little overlap in the transferred knowledge.

[0]: https://github.com/CTGT-Inc/lineage-eval/blob/main/data/benc...

[1]: https://github.com/CTGT-Inc/lineage-eval/blob/main/data/benc...

Censorship can be applied at the corpus level, though. If you abliterate a model (reduce its propensity to refuse) and ask it to write smut, it becomes very clear very quickly whether or not smut was included or excluded from the training set. It either mostly knows how sex works or very obviously doesn't. Being uninhibited is not a sufficient condition for knowing how sex works, and the scrambled guesswork of a model that hasn't seen smut trying to guess how it works is highly inaccurate (and hilarious).

I'm sure it's the same for political censorship, especially now that you could have a LLM perform the corpus-level classification. If the censors are lazy, abliteration is enough. If the censors are thorough, it isn't.

Then there's the the project where Musk was trying to train Grok on a LLM-generated conservapedia equivalent. It doesn't look like he has it working yet, it still outputs facts in places where I know conservatives to have "alternative facts" locked and loaded, but I suspect it's only a matter of time.