提问的艺术：面向合成数据的多语言提示优化
The Art of Asking: Multilingual Prompt Optimization for Synthetic Data
AI总结：
本文提出提示空间优化框架，通过自然性、文化适应和难度增强转换多语言提示，显著提升模型在多语言基准上的性能，实现更稳健且具文化根基的LLM。
摘要：
合成数据已成为扩展大型语言模型的基石，但其多语言使用仍受限于基于翻译的提示。这种策略继承了以英语为中心的框架和风格，忽视了文化维度，最终限制了模型的泛化能力。我们认为，被忽视的提示空间——即定义训练分布的输入——为提升多语言性能提供了更有力的杠杆。我们引入了一个轻量级的提示空间优化框架，其中翻译后的提示被系统地转换，以提升自然性、文化适应性和难度增强。使用现成的多语言LLM，我们将这些转换应用于涵盖7个语系的12种语言的提示。在相同的数据条件下，我们的方法相对于仅翻译的基线在下游任务上取得了显著且一致的改进：Global-MMLU准确率提升+4.7%，Flores XCometXL提升+2.4%，mArenaHard偏好胜率提升+35.3%。我们将提示空间优化确立为一种简单而强大的范式，用于构建更稳健、更具文化根基且具备全球能力的大型语言模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
