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This research identifies critical reliability risks in multilingual large language models, revealing how instruction hierarchy compliance varies asymmetrically across languages and domains. The study introduces XIH-Bench to demonstrate that cross-language conflicts can paradoxically improve safety while same-language specialization creates new security vulnerabilities for lower-priority commands. These findings are essential for developers deploying AI systems globally who must ensure consistent behavior regardless of the user's language or model training distribution.
Language Shapes Instruction Hierarchy Compliance in Multilingual LLMs
arXiv:2607.23545v1 Announce Type: new Abstract: Instruction hierarchy (IH) requires models to prioritize instructions by source, ensuring that higher-priority instructions override lower-priority ones. Despite its importance for safe and controllable deployment, existing evaluations have focused almost exclusively on English, leaving it unclear whether IH compliance remains stable in multilingual settings. We introduce XIH-Bench, a benchmark for multilingual IH evaluation with both same-language and cross-language conflicts across six languages, four domains, and three IH settings. Across models, we find two consistent patterns. First, IH compliance exhibits a clear language-dependent asymmetry: a language that strengthens compliance in the higher-priority position can become disruptive in the lower-priority position. Second, cross-language conflicts yield higher compliance than same-language conflicts, a phenomenon we term the Language Boundary Effect. We further show that language specialization can make lower-priority instructions in model-favored languages harder to override, creating multilingual reliability and security risks.
Abstract:Instruction hierarchy (IH) requires models to prioritize instructions by source, ensuring that higher-priority instructions override lower-priority ones. Despite its importance for safe and controllable deployment, existing evaluations have focused almost exclusively on English, leaving it unclear whether IH compliance remains stable in multilingual settings. We introduce XIH-Bench, a benchmark for multilingual IH evaluation with both same-language and cross-language conflicts across six languages, four domains, and three IH settings. Across models, we find two consistent patterns. First, IH compliance exhibits a clear language-dependent asymmetry: a language that strengthens compliance in the higher-priority position can become disruptive in the lower-priority position. Second, cross-language conflicts yield higher compliance than same-language conflicts, a phenomenon we term the Language Boundary Effect. We further show that language specialization can make lower-priority instructions in model-favored languages harder to override, creating multilingual reliability and security risks.
Computation and Language (cs.CL)
arXiv:2607.23545 [cs.CL]
(or arXiv:2607.23545v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.23545 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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