@inproceedings{wang-etal-2025-human,
    title = "Human-Inspired Obfuscation for Model Unlearning: Local and Global Strategies with Hyperbolic Representations",
    author = "Wang, Zekun  and
      Zeng, Jingjie  and
      Li, Yingxu  and
      Yang, Liang  and
      Lin, Hongfei",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.774/",
    doi = "10.18653/v1/2025.findings-emnlp.774",
    pages = "14354--14366",
    ISBN = "979-8-89176-335-7",
    abstract = "Large language models (LLMs) achieve remarkable performance across various domains, largely due to training on massive datasets. However, this also raises growing concerns over the exposure of sensitive and private information, making model unlearning increasingly critical.However, existing methods often struggle to balance effective forgetting with maintaining model utility. In this work, we propose HyperUnlearn, a human-inspired unlearning framework. We construct two types of fuzzy data{---}local and global{---}to simulate forgetting, and represent them in hyperbolic and Euclidean spaces, respectively. Unlearning is performed on a model with frozen early layers to isolate forgetting and preserve useful knowledge.Experiments demonstrate that HyperUnlearn effectively forgets sensitive content while maintaining the model{'}s language understanding, fluency, and benchmark performance, offering a practical trade-off between forgetting and capability preservation."
}