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Wouldn’t intuition suggest better performance with at review than at approval?
What about Switch Transformers?
Please add any questions in the chat. Thanks!
Switch transformer model weights aren't yet released by Google, but would be cool!
Is it fair to interpret your results as saying that development agencies can focus more on the more important features you find (e.g. abstracts and reviews) to determine project success? Assuming the people game the system problem doesn’t exist. If this is an oversimplification, how is it so?
Potentially multiple WB projects are implemented in the same sector in a single country at the same time - how may have this influenced the outcome variable?
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Is there any intuition WHY the larger models are actually more interpretable? Would’ve thought more parameters/embedding dimensions might make it more difficult.
I’m also a big fan of “Don’t Build It” — thanks for that! I’m wondering if emphasis placed in that piece (as I recall) on local ownership and designing with users, etc., could be captured in this analysis. In other words, can you say anything about whether there’s a correlation between locally led development and positive outcomes?
And could you share a few practicalities on your current implementation, e.g. training time, inference speed, GPU power/memory, maybe cloud TPUs?
(FYI, here’s a link to Luke’s Don’t Built It guide in case you want to check it out later: https://mitgovlab.org/resources/dont-build-it-a-guide-for-practitioners-in-civic-tech/)
One more question - since documentation appears paramount. Might some regions be disadvantaged due to poorer documentation
Thank you Lily
Thank you all!