arXiv AI By Pinaki Mohanty, Haoran Tang, Maggie Makar, Rajiv Khanna

Learning What to Forget: Distributional Unlearning for LLM Representation Spaces

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arXiv AI
1d ago

Linguistic Loopholes in LLM Unlearning: From a 174-Language Benchmark to Coverage-Aware Unlearning

The paper introduces the problem of cross‑lingual loopholes in large language model (LLM) unlearning, where forgetting a fact in one language can leave it accessible in others. It presents a new 174‑language benchmark, the Cross‑Lingual Unlearning Tensor, and proposes COVER, a method that selects a subset of source languages to maximize unlearning coverage under a language budget. Experiments show COVER reduces residual knowledge by 7.8–27.3% compared to uniform selection and works on both synthetic and real low‑resource news data.

By Tyler Skow, Shravan Chaudhari, Rama Chellappa, Abhay Yadav
arXiv Machine Learning
Jun 25

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning

arXiv:2510. 04773v2 Announce Type: replace Abstract: As Large Language Models (LLMs) demonstrate remarkable capabilities learned from vast corpora, concerns regarding data privacy and safety are receiving increasing attention.

By Kai Qin, Jiaqi Wu, Jianxiang He, Haoyuan Sun, Yifei Zhao, Xu Wang, Bin Liang, Yongzhe Chang, Cheng Li, Tiantian Zhang, Houde Liu