Quantization-Robust LLM Unlearning via Low-Rank Adaptation

João Vitor Boer Abitante; Joana Meneguzzo Pasquali; Luan Fonseca Garcia; Ewerton de Oliveira; Thomas da Silva Paula; Rodrigo C. Barros; Lucas S. Kupssinskü

arXiv · 2602.13151

Quantization-Robust LLM Unlearning via Low-Rank Adaptation

João Vitor Boer Abitante·Joana Meneguzzo Pasquali·Luan Fonseca Garcia·Ewerton de Oliveira·Thomas da Silva Paula·Rodrigo C. Barros·Lucas S. Kupssinskü

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Abstract

Large Language Model (LLM) unlearning aims to remove targeted knowledge from a trained model, but practical deployments often require post-training quantization (PTQ) for efficient inference. However, aggressive low-bit PTQ can mask unlearning updates, causing quantized models to revert to pre-unlearning behavior. We show that standard full-parameter fine-tuning often induces parameter changes that are too small to survive 4-bit quantization. We propose quantization-robust unlearning via low-rank adaptation (LoRA): we freeze the base model and concentrate unlearning into trainable adapters so that the effective update is preserved after quantization. On Llama-2-7B evaluated with MUSE dataset (BOOKS and NEWS), LoRA improves 4-bit utility by up to 7.93 points (NPO+GDR on BOOKS: 50.17 to 58.10) and yields higher 4-bit utility on NEWS for GA+GDR (40.06 to 44.82, increase of 4.76). LoRA also substantially reduces privacy leakage under 4-bit PTQ, e.g., for GA+KLR on BOOKS, PrivLeak moves from -25.68 to -5.86 (closer to ideal 0), while maintaining strong forgetting (VerMem and KnowMem near 0). Thus, using LoRA for Machine Unlearning is beneficial for scenarios where quantization is necessary for model deployment.

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João Vitor Boer Abitante, Joana Meneguzzo Pasquali, Luan Fonseca Garcia, Ewerton de Oliveira, Thomas da Silva Paula, Rodrigo C. Barros, Lucas S. Kupssinskü. 2026-04-07. Quantization-Robust LLM Unlearning via Low-Rank Adaptation. https://arxiv.org/abs/2602.13151

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