Links, if you don't mind?
Inquisitive minds wish to know.
Inquisitive minds wish to know.
If you're going to claim it exists, do "the class" a solid by sharing. I, for one, am highly interested in anything that can reverse from blob -> potential source characterization.
SHAP is a great tool for unpacking deep nets on image recognition. Show which pixels mattered and which did not. Very cool.
Various dimension reduction or attention tracking or whatever exists for unpacking generate text and what not.
If you want to understand the nuance of a particular output you’re best off looking at the distribution of next tokens and their pre-temperature weighted sampling.
Different questions require different tools. Why did the model choose that phrasing? Why did the model choose this answer instead of that answer? What made the model make a certain claim?
A lot of it will be driven by the context assuming that the model works. So if you ask “Is X good or bad and why” the majority of text will be on the “why”, but the generation of that text will be contingent on the initial tokens that determined if it should say “good or bad” first. The model does not come up with a reasoning for its assertions, it comes up with an assertion and then creates a reasoning to defend it (in this question format at least). So perhaps that is all you care about?
Anyway, it depends on the question.
Breadcrumbs m'fellow. The quest for knowledge is made easier for everyone involved when we use nouns instead of assuming the other people in the room have access to the contents of our heads.
The "is it good and why" part we can read for ourselves. It's the name to search for to get started on the path that's important.
Apologies, though, t'was only meant to be a slight nudge. That post might have picked up a bit more acidity than warranted. Probably because missing antecedents are my #1 pet peeve.