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2 min in and I have no idea what I am watching..
Paging /u/SolidGoldMagikarp
Doesn’t look like anything to me
@12:00 made me think of Everything, Everywhere, All at Once
TL;DW language models spit out weird results when given tokens that don’t have a similarity/meaning cluster. Tokens are supposed to represent frequently-occurring data but due to bad sampling and data culling these tokens represent rarely-occurring data. When the model encounters them, it gives very precise but very bad results, because they’re not “near” any meaningful/similar clusters.