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This paper introduces Null-Space Constrained Response-Specified Unlearning (NSRU), a low-rank framework that uses orthogonal-projected LoRA updates confined to the null space of retain subspaces to perform controlled LLM unlearning while preserving benign capabilities.
This paper studies multilingual unlearning in LLMs by extending the TOFU benchmark to five languages. It finds that unlearning transfer varies by script and family, operates primarily in later decoding layers, and that a single steering direction can recover much of the suppressed knowledge across languages.