Human-Inspired Obfuscation for Model Unlearning: Local and Global Strategies with Hyperbolic Representations

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 Hy-perUnlearn effectively forgets sensitive content while maintaining the models language understanding, fluency, and benchmark performance, offering a practical trade-off between forgetting and capability preservation.