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arXiv Computation and Language By Guoshenghui Zhao, Huawei Lin, Weijie Zhao

RapidUn: Influence-Driven Parameter Reweighting for Efficient Large Language Model Unlearning

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RapidUn is a parameter reweighting framework that uses influence estimates to guide LoRA-only updates for efficient unlearning of targeted behaviors in large language models. It operates in a practical PEFT setting with a small forget set and limited retain buffer, converting cross-sample influence into fixed sample-specific weights for weighted LoRA unlearning. Experiments on Llama‑3‑8B with Dolly‑15k and Alpaca‑57k datasets show RapidUn achieves lower trigger ASR than Fisher, GA, and LoReUn while preserving clean utility, and delivers a 77× wall‑clock speedup over clean‑corpus LoRA retraining, with additional evaluations supporting its effectiveness.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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