<ul><li>Researchers from Meta, DeepMind, Cornell, and NVIDIA released a paper titled 'How Much Do Language Models Memorize?' in June 2025.</li><li>The paper introduces a framework for measuring memorization in language models and its relation to model size, data scale, and privacy risk.</li><li>It proposes an information-theoretic definition of memorization grounded in Kolmogorov complexity, separating memorization from generalization.</li><li>The study examined transformer models on synthetic and real text data to understand how models memorize and transition to generalization as datasets grow.</li></ul>

Does a LLM Memorize Private or Sensitive Data from Training?

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