arXiv AI October 1, 2026 By Tyler Skow, Shravan Chaudhari, Rama Chellappa, Abhay Yadav

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.

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arXiv AI

Sep 10

Beyond Cross-Lingual Transfer: Benchmarking Propagation Boundaries in Multilingual LLM Unlearning

The paper introduces CLLPU, a multilingual benchmark for evaluating how well large language models can unlearn specific knowledge while controlling its propagation across languages. CLLPU defines two forgetting scenarios—common-goal forgetting, which requires suppression across all languages, and language-conditioned forgetting, which limits suppression to a single language. Using 800 knowledge-unit pairs and 72,000 QA instances in ten languages, the authors test six methods on Llama‑3.1‑8B‑Instruct and find that universal suppression often fails, while language‑specific suppression can unintentionally spread to other languages, highlighting the difficulty of propagation control in multilingual unlearning.

By Pengyang Shao, Chuanpeng Lu, Wei Qin, Yanzheng Jin, Xiaohao Liu, Xi Ai, Kenji Kawaguchi, Richang Hong

llms benchmarks

arXiv Machine Learning

Jul 13

Forget Narrowly, Retain Broadly: Unlearning as an Asymmetric Generalization Problem

arXiv:2607. 09236v1 Announce Type: new Abstract: Machine unlearning in LLMs is the targeted removal of specific knowledge while preserving all other capabilities, critical for privacy and safety.

By Amit Peleg, Naman Deep Singh, Naama Pearl, Bibhabasu Mohapatra, Matthias Hein

llms benchmarks safety

arXiv Machine Learning

Jun 5

Learning What to Forget: Improving LLM Unlearning via Learned Token-Level Importance

arXiv:2606. 06320v1 Announce Type: new Abstract: Machine unlearning aims to remove targeted knowledge from a trained model while preserving its general capabilities.

By Gizem Y\"uce, Giorgos Nikolaou, Nicolas Flammarion

llms

arXiv Machine Learning

2d ago

UnlearningSoup: Is Repeated Tuning Necessary for Large Language Model Unlearning?

arXiv:2609.37076v1 Announce Type: new Abstract: Large language models trained on vast corpora inherently risk memorizing harmful content that may later re-emerge in their outputs. To mitigate this is...

By Puning Yang, Qizhou Wang, Junchi Yu, Bo Han, Xiuying Chen

llms

arXiv Computation and Language

Sep 21

$\mu^2$-Bench: A Multilingual Machine Unlearning Benchmark

arXiv:2609. 20945v1 Announce Type: new Abstract: Undesired information such as harmful content and private data propagates through Multilingual Large Language Models (LLMs) via direct training and indirect cross-linguistic spread.

By Kyomin Hwang, Hyeonjin Kim, Hyunho Lee, Yearim Kim, Yeji Song, Nojun Kwak

llms benchmarks

arXiv Machine Learning

Aug 4

LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering

arXiv:2604. 03532v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show strong multilingual capabilities, yet reliably controlling the language of their outputs remains difficult.

By Sing Hieng Wong, Hassan Sajjad, A. B. Siddique

llms benchmarks

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Related stories

Beyond Cross-Lingual Transfer: Benchmarking Propagation Boundaries in Multilingual LLM Unlearning

Forget Narrowly, Retain Broadly: Unlearning as an Asymmetric Generalization Problem

Learning What to Forget: Improving LLM Unlearning via Learned Token-Level Importance

UnlearningSoup: Is Repeated Tuning Necessary for Large Language Model Unlearning?

$\mu^2$-Bench: A Multilingual Machine Unlearning Benchmark

LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering