UPDESH: 为13种印地语系语言合成基于现实的指令微调数据
UPDESH: Synthesizing Grounded Instruction Tuning Data for 13 Indic Languages
AI总结：
UPDESH通过基于维基百科内容的自下而上方法，生成高质量的多语言指令数据，提升多语言AI系统的文化适应性与性能。
摘要：
开发具有文化根基的多语言AI系统仍面临挑战，特别是对于低资源语言。虽然合成数据具有潜力，但其在多语言和多文化环境中的有效性尚待探索。我们研究了基于大规模开源LLM（参数≥235B）的自下而上合成数据生成方法，该方法基于语言特定的维基百科内容，补充了主流的自上而下翻译方法。我们引入了Updesh，一个高质量的大规模合成指令遵循数据集，包含950万个数据点，覆盖13种印度语言和英语，涵盖多样化的推理和生成任务。通过自动化指标和10000次人工评估的全面评估确认了高质量的数据。通过在各种数据集上微调模型并评估在13种多样化的多语言数据集和模型比较评估中的性能，证明了在NLU和NLG评估中，训练Updesh的模型始终获得显著提升。最后，通过消融研究和文化评估，我们展示了上下文感知、文化扎根的数据生成对于有效多语言AI开发至关重要。
英文摘要：
Developing culturally grounded multilingual AI systems remains challenging, particularly for low-resource languages. While synthetic data offers promise, its effectiveness in multilingual and multicultural contexts is underexplored. We investigate bottom-up synthetic data generation using large open-source LLMs (>= 235B parameters) grounded in language-specific Wikipedia content, complementing dominant top-down translation-based approaches from English. We introduce Updesh, a high-quality large-scale synthetic instruction-following dataset comprising 9.5M data points across 13 Indian languages and English, encompassing diverse reasoning and generative tasks. Comprehensive evaluation using automated metrics and 10K human assessments confirms high data quality. Downstream evaluations performed by fine-tuning models on various datasets and assessing performance across 13 diverse multilingual datasets and model comparative evaluations, demonstrate that models trained on Updesh consistently obtain significant improvements on NLU, NLG evaluations. Finally, through ablation studies and cultural evaluations, we show that context-aware, culturally grounded data generation is essential for effective multilingual AI development.
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