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原文摘要

Synthetic data has become a cornerstone for scaling LLMs, yet its benefits for multilingual modeling remain bottlenecked by translation. Translation for data augmentation inherits English-centric framing and style and neglects cultural dimensions, ultimately constraining model generalization. We argue that the overlooked prompt space offers a more powerful lever for improving multilingual performance. We introduce a lightweight framework for prompt-space optimization, where translated prompts are systematically transformed for Naturalness, Cultural Adaptation, and Difficulty Enhancement. Using an off-the-shelf multilingual LLM, we apply these transformations to prompts from 12 languages spanning 7 families. Under identical data conditions, our approach achieves substantial and consistent downstream improvements over the translation-only baseline: +4.7% accuracy on Global-MMLU, +2.4% XCometXL on Flores and +35.3% wins in preferences on mArenaHard. These gains are further corroborated by human preferences on open-ended creative writing. We establish prompt-space optimization as a simple yet powerful paradigm for building multilingual LLMs that are more robust, culturally grounded, and globally capable.