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Depending on the task. When you are trying to solve a crime, you only know that the murderer was a nurse, it's very important to assume a valid p(murderer|gend
by vletal 3y ago
Depending on the task.
When you are trying to solve a crime, you only know that the murderer was a nurse, it's very important to assume a valid p(murderer|gender) as well as p(gender|nurse).
On the other hand, leaking p(gender|nurse) into a candidate scoring algorithm would be a no-no.
What people seem to ask for is very interesting actually. To both learn the underlying statistics AND learn not to use it explicitly in speech at the same time. Assuming next token prediction is the learned function, these two feel a bit contradictory.
- sega_sai 3y agoI absolutely agree. Probabilistic statements are fine and useful. Assumptions X equals Y in speech probably not. How to exactly address this is not quite clear, that's why I thought the article was quite interesting. (and obviously fixing the labelling of professor = man in text generation is actually much less useful for gender equality say than dealing with actual issues that lead to the P(X|category1)!=P(X|category2))