The Art of Asking: Multilingual Prompt Optimization for Synthetic Data

TL;DR

This paper proposes a prompt-space optimization framework that systematically transforms multilingual prompts to improve large language model performance across diverse languages and cultural contexts, surpassing translation-based methods.

Contribution

It introduces a lightweight prompt-space optimization approach that enhances multilingual LLMs by improving prompt naturalness, cultural adaptation, and difficulty, demonstrating significant performance gains.

Findings

+4.7% accuracy on Global-MMLU

+2.4% on Flores XCometXL

+35.3% wins in preferences on mArenaHard

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.