It doesn't necessarily have to shift away from diversity biases. I think with care, you can preserve the biases that matter most. That was just their first shot at it, this seems like something you'd get better at over time.
I guess their main shortcoming was the cultural training set. I'm still unconvinced that level of fine tuning is possible without increasing model size, but we'll see what happens if/when someone curates a much larger set with cultural labeling.
The labels might also need to be more granular, like "culture:subculture:period", or something... which is kind of a snakes nest by itself.
It doesn't necessarily have to shift away from diversity biases. I think with care, you can preserve the biases that matter most. That was just their first shot at it, this seems like something you'd get better at over time.
I guess their main shortcoming was the cultural training set. I'm still unconvinced that level of fine tuning is possible without increasing model size, but we'll see what happens if/when someone curates a much larger set with cultural labeling.
The labels might also need to be more granular, like "culture:subculture:period", or something... which is kind of a snakes nest by itself.