What LLMs Remember—and Why: Unpacking the Entropy-Memorization Law
TL;DR for operators Memorization audits usually start with the wrong question: “Which individual text snippets look memorized?” This paper suggests a better first diagnostic: group many snippets by how closely the model reproduces them, then measure the entropy of the token distribution inside each group.1 The result is an empirical pattern the authors call Entropy–Memorization Linearity. In plain English: when training examples are pooled by edit-distance score, their set-level entropy forms a strong linear relationship with how closely the model reproduces them. Since the paper’s “memorization score” is an edit distance, lower score means stronger verbatim reproduction; higher score means the generated continuation is farther from the ground truth. ...