Honestly, I'll take it a step further: what's wrong with training?
Having principles means applying them uniformly -- even to large entities or those you hate (it's fine if the principles themselves have size bounds in them, though -- versus them being implicitly glued on -- but then you need a universal justification for why that size. Which is possible and valid.)
I think that one should use the best algorithms, the best information, the best knowledge they have access to -- period. I don't like using gimped machines, I don't like making gimped machines, and I certainly don't like being sold them.
So I'm not going to turn around and say LLMs need to be gimped via arbitrary restrictions on their training data.
Having principles means applying them uniformly -- even to large entities or those you hate (it's fine if the principles themselves have size bounds in them, though -- versus them being implicitly glued on -- but then you need a universal justification for why that size. Which is possible and valid.)
I think that one should use the best algorithms, the best information, the best knowledge they have access to -- period. I don't like using gimped machines, I don't like making gimped machines, and I certainly don't like being sold them.
So I'm not going to turn around and say LLMs need to be gimped via arbitrary restrictions on their training data.