How Learning Governs Unlearning across the Memorization-Generalization Spectrum

Key points

• While unlearning seeks to negate undesired capabilities acquired through learning, little research has examined how the way models learn shapes their subsequent unlearning.1
• We 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.1
• Furthermore, we conduct a finer-grained analysis by introducing bucketed modular addition, in which the respective contributions of the two strategies can be explicitly controlled across the memorization-generalization spectrum.1
• We further demonstrate that this relationship also holds in LLM unlearning across verbatim and factual recall settings.1

Sources (1)

• [1]How Learning Governs Unlearning across the Memorization-Generalization Spectrum
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 03:50 PM

  In this paper, we investigate this connection from the perspectives of memorization and generalization, the two most representative yet competing strategies that models employ during training.

  While unlearning seeks to negate undesired capabilities acquired through learning, little research has examined how the way models learn shapes their subsequent unlearning.

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