选语言没标准答案，多语言训练反而可能变差。
No Optimal Language Set Exists for Multilingual Instruction Tuning: Insights from a Linguistically-Informed Study
- 按语言类型、地理分布等特征选语言，对比随机选择
- 模型和任务不同，最佳语言组合也不同，加语言会恶化性能
- 适合关注多语言训练数据设计的研究者和实践者
多语言指令微调（MIT）面临多语言诅咒、数据稀缺和高计算成本的挑战。一个自然假设是：精心挑选语言多样性高的语言集能提升通用性能。我们系统评估了基于语言类型、地理分布、语义和学习特征的选语言策略，对比随机基线，在三种模型（mGPT、mT5-xl、BLOOM）和五个多语言基准上进行测试。关键发现是：在固定预算下，不存在普适最优的语言选择策略——性能高度依赖任务与模型；超过适度语言数量后，性能反而下降，符合多语言诅咒现象。研究讨论了对数据构建的影响，以及基准平均评价的潜在陷阱。所有资源已公开于 https://github.com/GGLAB-KU/ling-informed-mit。
原文摘要 · Abstract (English)
Multilingual instruction tuning (MIT) is challenged by the curse of multilinguality, data scarcity, and high computational cost. A natural hypothesis is that carefully selecting a linguistically diverse set of languages yields universally better models. We test this systematically by evaluating linguistically-informed selection strategies---based on typological, geographical, semantic, and learned features---against random baselines across three model families (mGPT, mT5-xl, BLOOM) and five multilingual benchmarks. Our key negative finding is that no universal language selection strategy emerges in our fixed-budget setting: performance is strongly task- and model-dependent, and adding more languages beyond a modest threshold triggers degradation consistent with the curse of multilinguality. We discuss implications for MIT data curation and the pitfalls of benchmark-averaged evaluation. All resources are publicly available at https://github.com/GGLAB-KU/ling-informed-mit.
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