When Memorizing Irrelevant Data Becomes Necessary

Created in December 11, 2023

2023   ·   llm   theory   ·   paper-review

This paper investigates a nuanced memorization phenomenon: under certain data distribution conditions, memorizing irrelevant training samples (examples not representative of the test distribution) can be necessary to achieve high accuracy.

The result challenges the intuitive view that memorization is always harmful or that good generalization requires only learning relevant patterns. Instead, the analysis shows that:

  • In certain settings, irrelevant memorized data acts as implicit regularization
  • The boundary between “memorized” and “generalized” knowledge is less clean than commonly assumed
  • These findings have direct implications for understanding LLM training dynamics and privacy risks


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