@inproceedings{ni-etal-2025-controllable,
    title = "Controllable Memorization in {LLM}s via Weight Pruning",
    author = "Ni, Chenjie  and
      Wang, Zhepeng  and
      Bao, Runxue  and
      Gao, Shangqian  and
      Zhang, Yanfu",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.765/",
    pages = "15141--15156",
    ISBN = "979-8-89176-332-6",
    abstract = "The evolution of pre-trained large language models (LLMs) has significantly transformed natural language processing. However, these advancements pose challenges, particularly the unintended memorization of training data, which raises ethical and privacy concerns. While prior research has largely focused on mitigating memorization or extracting memorized information, the deliberate control of memorization has been underexplored. This study addresses this gap by introducing a novel and unified gradient-based weight pruning framework to freely control memorization rates in LLMs. Our method enables fine-grained control over pruning parameters, allowing models to suppress or enhance memorization based on application-specific requirements. Experimental results demonstrate that our approach effectively balances the trade-offs between memorization and generalization, with an increase of up to 89.3{\%} in Fractional ER suppression and 40.9{\%} in Exact ER amplification compared to the original models."
}