AI RESEARCH
Unlearning What Matters: Token-Level Attribution for Precise Language Model Unlearning
arXiv CS.CL
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ArXi:2605.00364v1 Announce Type: new Machine unlearning has emerged as a critical capability for addressing privacy, safety, and regulatory concerns in large language models (LLMs). Existing methods operate at the sequence level, applying uniform updates across all tokens despite only a subset encoding the knowledge targeted for removal. This