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This paper first classify memorization- and generalization-heavy models using grokking in modular addition and compare their responses to unlearning, showing that the latter suffer greater retain damage, i.e., a larger performance drop on the retain set.
It is argued that effective governance of OWFMs requires a multi-layered approach integrating three complementary components: model cards, acceptable use policies (AUPs), and licenses, and that standard open-source licenses are not well suited for OWFMs and may weaken the enforceability of AUPs.
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