Translation as a Scalable Proxy for Multilingual Evaluation

• URL: http://arxiv.org/abs/2601.11778v1
• Date: Fri, 16 Jan 2026 21:01:40 GMT
• Title: Translation as a Scalable Proxy for Multilingual Evaluation
• Authors: Sheriff Issaka, Erick Rosas Gonzalez, Lieqi Liu, Evans Kofi Agyei, Lucas Bandarkar, Nanyun Peng, David Ifeoluwa Adelani, Francisco Guzmán, Saadia Gabriel,
• Abstract summary: We evaluate the validity of a simpler alternative: can translation quality alone indicate a model's broader multilingual capabilities?<n>We find that translation performance is a good indicator of downstream task success.
• Score: 41.29816736489213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid proliferation of LLMs has created a critical evaluation paradox: while LLMs claim multilingual proficiency, comprehensive non-machine-translated benchmarks exist for fewer than 30 languages, leaving >98% of the world's 7,000 languages in an empirical void. Traditional benchmark construction faces scaling challenges such as cost, scarcity of domain experts, and data contamination. We evaluate the validity of a simpler alternative: can translation quality alone indicate a model's broader multilingual capabilities? Through systematic evaluation of 14 models (1B-72B parameters) across 9 diverse benchmarks and 7 translation metrics, we find that translation performance is a good indicator of downstream task success (e.g., Phi-4, median Pearson r: MetricX = 0.89, xCOMET = 0.91, SSA-COMET = 0.87). These results suggest that the representational abilities supporting faithful translation overlap with those required for multilingual understanding. Translation quality, thus emerges as a strong, inexpensive first-pass proxy of multilingual performance, enabling a translation-first screening with targeted follow-up for specific tasks.

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