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Tian-Long Chen

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#artificial intelligence Preprint Sep 2026

Quantization-Robust Unlearning through the Lens of Retain-Forget Loss Landscapes Interaction

Unlearning ensures LLM compliance by removing the influence of private or copyrighted training data. However, since LLM models typically undergo post-training compression, like quantization, in practical deployment, it has been observed that the unlearning effect can be substantially weakened, with the forgetting behav...

Jia-Lu Wang, Jia-Ning Deng, Shu-Qing Luo et al. · 1 citation
#machine learning Preprint Sep 2026

When Retain Constraints Conflict: Mitigating Forget-Retain Interference in Tabular Data

Machine unlearning aims to remove the influence of designated training data while preserving model utility, but its behavior on tabular data remains underexplored. This gap is important because tabular prediction is widely used in high-stakes domains and is increasingly adapted to language models through record seriali...

Zijie Liu, Jinhao Duan, Bing-Qi Shang et al. · 0 citations

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