Large language models (LLMs) can memorize and reproduce training sequences verbatim, raising privacy and copyright concerns. Existing mitigation methods apply interventions uniformly, often degrading predictions at the many positions unrelated to verbatim recall. We study a token-level log-likelihood ratio between a target model and a domain-competent reference with lower exposure to the evaluated sequences. Exposure-controlled experiments support its use for localizing positions associated with sequence-level verbatim recall. Across evaluated settings, a minority of positions carries a disproportionate share of the positive signal, motivating selective activation correction. From a first-order surrogate of the memorization-reduction objective, we derive a probe-steer framework, which decomposes intervention into a probe that flags high-signal activations and a steer that applies a correction only when the probe exceeds a threshold. We propose Gated Subspace Steering (GSS) as its practical instantiation: under a background normalization constraint, the probe-steer pairs that are optimal for this surrogate follow in closed form from the leading singular vectors of a whitened gradient-weighted activation matrix. Across four benchmarks, GSS achieves memorization suppression comparable to strong baselines, with favorable utility trade-offs in several settings, while requiring 10-100$\times$ less compute than optimization-based alternatives. Code is available at https://github.com/ubc-tea/Mitigating-Memorization-Where-It-Happens.
Understanding and Mitigating Memorization in Generative Models via Sharpness of Probability Landscapes
Department of Computer Science, Yonsei University, Seoul, Korea（计算机科学系，延世大学，首尔，韩国）；Department of Artificial Intelligence, Yonsei University, Seoul, Korea（人工智能系，延世大学，首尔，韩国）
Understanding and Mitigating Memorization in Diffusion Models for Tabular Data
Department of Computer；Data Sciences, Case Western Reserve University, Cleveland, USA；Department of Computer Science \& Engineering, Texas A\&M University, College Station, USA；Department of Biochemistry, Case Western Reserve University, Cleveland, USA；Center for RNA Science；Therapeutics, Case Western Reserve University, Cleveland, USA；Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
University of Chicago（芝加哥大学）；Argonne National Laboratory（阿贡国家实验室）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Dartmouth College（达特茅斯学院）；International Computer Science Institute（国际计算机科学研究所）；University of California, Berkeley（加州大学伯克利分校）
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction
Southern University of Science and Technology（南方科技大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；South China University of Technology（华南理工大学）；Nanyang Technological University（南洋理工大学）；RIKEN AIP（理化学研究所人工智能研究中心）
CrossSplit: Mitigating Label Noise Memorization through Data Splitting
Samsung Advanced Institute of Technology (SAIT)（三星高级技术研究院（SAIT））；SAIT AI Lab（三星高级技术研究院人工智能实验室）；Mila（米拉研究所）；Université de Montreal（蒙特利尔大学）；Canada CIFAR AI Chair（加拿大CIFAR人工智能讲席）
