The research introduces CLLPU (Cross-Lingual and Language-Bound Protocol for LLM Unlearning), a benchmark designed to evaluate multilingual LLM unlearning beyond cross-lingual transfer. The benchmark utilizes two distinct settings: common-goal forgetting, aiming for suppression across all languages, and language-conditioned forgetting, restricting suppression to a specific language. CLLPU constructs 800 matched knowledge-unit pairs and 72,000 QA instances across ten languages, employing goal-guided topic pairing, schema-aware relation matching, and dual-anchor multilingual translation. Six representative methods were evaluated using Llama-3.1-8B-Instruct, revealing divergent failure modes. Specifically, universal suppression resulted in incomplete forgetting, while language-conditioned confinement caused propagation beyond the intended boundary. The research indicates that general multilingual utility can mask damage to neighboring knowledge. These findings highlight propagation control as a key challenge in multilingual LLM unlearning.
Source: https://arxiv.org/abs/2609.05976