These models are pretty much always extrapolating [0] Whether the extrapolation is crude/low-rank or astute/high-rank is a question of memorization vs generali

These models are pretty much always extrapolating [0] Whether the extrapolation is crude/low-rank or astute/high-rank is a question of memorization vs generalization. That gets into the question of whether or not the model is over-fitted or under-fitted. There are certain heuristics borrowed from high dimensional statistical physics that can be used to guess how good the test performance of a model will be on a typical task without even knowing what the test data is [1]. Originality for me means finding better answers to sub-tasks, and then combining those answers together in a better way. This is the nirvana of cross-entropy minimization - the emergence of capability results from gaining the ability to amass a wider range of skills, improving upon them, and percolating those improvements towards multiplying the leverage of other skills. How long such a thing can keep improving with current tech, who knows, but you should really think critically about whether that sounds just like interpolation through the corpus. [0] Learning in High Dimension Always Amounts to Extrapolation - https://arxiv.org/abs/2110.09485 https://arxiv.org/abs/2110.09485 [1] https://github.com/CalculatedContent/WeightWatcher https://github.com/CalculatedContent/WeightWatcher