The Art Of Asking Multilingual Prompt Optimization For Synthetic Data 2025 10 23

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Oct 23, 2025 The Art of Asking: Multilingual Prompt Optimization for Synthetic Data A new lightweight framework for prompt-space optimization improves multilingual performance in large language models by transforming translated prompts for naturalness, cultural adaptation, and difficulty enhancement, achieving substantial downstream improvements over translation-only baselines.

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Authors

David Mora, Viraat Aryabumi, Wei-Yin Ko, Sara Hooker, Julia Kreutzer, and Marzieh Fadaee.

Abstract

Synthetic data has become a cornerstone for scaling large language models, yet its multilingual use remains bottlenecked by translation-based prompts. This strategy inherits English-centric framing and style and neglects cultural dimensions, ultimately constraining model generalization. We argue that the overlooked prompt space-the very inputs that define training distributions-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 for 12 languages spanning 7 families. Under identical data conditions, our approaches achieve substantial and consistent downstream improvements over the translation-only baseline: +4.7% on Global-MMLU accuracy, +2.4% on Flores XCometXL and +35.3% wins in preferences on mArenaHard. We establish prompt-space optimization as a simple yet powerful paradigm for building multilingual LLMs that are more robust, culturally grounded, and globally capable.

Synthetic Data Generation multilingual Language Models

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