From Anchors to Supervision: Memory-Graph Guided Corpus-Free Unlearning for Large Language Models

View PDF HTML (experimental) Abstract:Large language models (LLMs) may memorize sensitive or copyrighted content, raising significant privacy and legal concerns. While machine unlearning has emerged as a potential remedy, prevailing paradigms rely on user-provided forget sets, making unlearning requests difficult to audit and exposing systems to secondary leakage and malicious abuse. We propose MAGE, a Memory-grAph Guided Erasure framework for user-minimized, corpus-free unlearning. Given only a lightweight user anchor that identifies a target entity, MAGE probes the target LLM to recover target-related memorization, organizes it into a weighted local memory graph, and synthesizes scoped supervision for unlearning. MAGE is model-agnostic, can be plugged into standard unlearning methods, and requires no access to the original training corpus. Experiments on two benchmarks, TOFU and RWKU, demonstrate that MAGE's self-generated supervision achieves effective unlearning performance comparable to supervision generated with external reference, while preserving overall utility. These results support a practical and auditable unlearning workflow driven by minimal anchors rather than user-supplied forget corpora. Comments: 15 pages, appendix included Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.13777 [cs.CL] (or arXiv:2604.13777v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2604.13777 arXiv-issued DOI via DataCite (pending registration) Submission history From: Wenxuan Li [view email] [v1] Wed, 15 Apr 2026 12:07:14 UTC (1,657 KB)