Scaling Multilingual Safety and Reasoning Evaluation through Reasoning-Guided Translation

Zahraa Al Sahili; Ioannis Patras; Matthew Purver

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arXiv · 2610.20221

Scaling Multilingual Safety and Reasoning Evaluation through Reasoning-Guided Translation

Zahraa Al Sahili·Ioannis Patras·Matthew Purver

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Abstract

Large language models (LLMs) are increasingly deployed across languages, yet their safety and reasoning capabilities remain predominantly evaluated in English. Translating existing benchmarks offers a scalable approach to multilingual evaluation, but preserving task-specific meaning across languages presents significant challenges, particularly for harmful requests, mathematical reasoning, and conversational interactions. We introduce a reasoning-guided translation framework for constructing multilingual evaluations designed to preserve the structure and intended meaning of diverse benchmark tasks. Our approach separates translation strategy selection from large-scale generation, combining reasoning-guided configuration selection with task-aware validation, targeted repair, and independent quality screening. Using this framework, we develop a source-aligned evaluation collection spanning seven safety, conversational, and mathematical reasoning resources across 11 languages. The resulting collection achieves 99.94\% coverage of intended item--language pairs in the two subsets requiring repair. Evaluations of open-source LLMs reveal distinct language-dependent vulnerabilities, including inconsistent refusal behaviour and substantial reasoning degradation in lower-resource languages. These findings highlight the importance of task-sensitive benchmark construction and systematic quality assessment for understanding multilingual model behaviour beyond English.

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Zahraa Al Sahili, Ioannis Patras, Matthew Purver. 2026-10-14. Scaling Multilingual Safety and Reasoning Evaluation through Reasoning-Guided Translation. https://arxiv.org/abs/2610.20221

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