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.
This survey provides the first comprehensive review of research on code-switched LLM safety and robustness evaluation, and aims to guide researchers and practitioners in developing more robust evaluation frameworks and alignment techniques for real-world multilingual deployments.
Pulagam Naveen Kumar, Sowjanya Bojja, K. Anoosha et al.· International Journal for Re...· 0 citations
System prompts are the primary lever practitioners use to control language model behavior, yet what they actually do to the computation inside the transformer remains poorly understood. Across 17 instruction-tuned models spanning 8 architecture families and 1.5B to 72B parameters, we use Centered Kernel Alignment (CKA)...
It is shown that user-preferring conflict resolution can coexist with a readable internal arbitration signal, and successful intervention depends on the geometry of the readout rather than probe accuracy alone, while directions selected mainly for pooled separability steer poorly.
Enrique Balp-Straffon, Chih-Hao Hsu, Rushiraj Gadhvi et al.· 0 citations
Skeleton-based reasoning prompting is a promising training-free approach for structuring LLM reasoning, but prior work largely assumes an English-centric setting. We propose the Language-Aware Skeleton Exploration Framework (LASEF) to study skeleton-language choice in multilingual mathematical reasoning. Across math be...
HyeonSeok Lim, Seung-Woo Song, Inho Won et al.· 0 citations
This work proposes aligning cross-lingual thoughts and responses (ACTR), a framework that improves multilingual safety alignment by strengthening the use of existing safety reasoning, and devise neuron-selective consistency optimization (NSCO), which uses a frozen judge model to reward agreement between the safety cate...
Xian-Hui Zhang, Jian Yu, Cheng-Yu Xie et al.· 0 citations
This work investigates cross-lingual safety transfer in four African languages, Twi, Hausa, Amharic, and Swahili, using LoDNA, a new safety dataset that pairs literal translations with culturally localized prompts to demonstrate superficial safety alignment.
Abigail Oppong, P SAM SAHIL, Tadesse Destaw Belay et al.· 1 citation
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