That’s my point. They claim to reduce misrepresentation, while at the same time they erase a bunch of correct representations.
Going back to what I was saying: fine tuning doesn’t increase diversity, it only shifts the biases. Encoding actual diversity would require increasing the model, then making sure it can output every correct representation.
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.
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“Indian” is a huge population of very diverse people.
That’s my point. They claim to reduce misrepresentation, while at the same time they erase a bunch of correct representations.
Going back to what I was saying: fine tuning doesn’t increase diversity, it only shifts the biases. Encoding actual diversity would require increasing the model, then making sure it can output every correct representation.
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.