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by K0balt
328 days ago
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I think an often overlooked aspect of training data curation is the value of accurate but oblique data. Much of the “emergent capabilities “ of LLMs comes from data embedded in the data, implied or inferred semantic information that is not readily obvious. Extraction of this highly useful information, in contrast to specific factoids, requires a lot of off axis images of the problem space, like a CT scan of the field of interest. The value of adjacent oblique datasets should not be underestimated. |
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You are may find a definition of what a "skyscraper" is, by some hyperfocused association, but you'll get a bias towards a definite measurement like "skyscrapers are buildings between 700m to 3500m tall", which might be useful for some data mining project, but not at all what people mean by it.
The actual definition is not in a specific source but in the way it is used in other sources like "the Manhattan skyscraper is one of the most iconic skyscrapers", on the aggregate you learn what it is, but it isn't very citable on its own, which gives WP that pedantic bias.