AI 中文总结
本文首次以公平性视角评估跨语言迁移中的源语言排序方法，发现语言距离（尤其复合距离和训练排序器）能显著减少资源与任务不平等，为公平高效的迁移语言选择提供实用基础。
AI 中文摘要
跨语言迁移强烈依赖于源语言的选择方式，但为每种目标语言确定最佳候选源语言是不切实际的，尤其是对于低资源目标语言。语言距离因其与迁移效果的相关性以及在资源稀缺场景中的适用性，被广泛用于对候选源语言进行排序。然而，基于距离的排序器在不同任务和资源水平下的可靠性仍未得到充分探索。因此，我们首次提出了以公平性为重点的源语言排序范式评估，研究了跨十项跨语言任务和两个多语言模型中的资源水平不平等和任务不平等。虽然这两种不平等在单个语言距离和英语始终作为基线的设置中最为显著，但通过无需训练的复合距离可大幅减少不平等，而经过训练的排序器几乎可以消除不平等。我们进一步证明了使用语言距离的排序器相对于使用语言模型内部表示的排序器的可靠性。总体而言，我们发现语言距离为公平且高效的迁移语言选择提供了实用基础。我们建议在可获得任务特定迁移评估时使用经过训练的排序器，否则使用复合距离。
英文摘要
Cross-lingual transfer is strongly conditional on how the source language is chosen, but it is impractical to determine the best candidate source for every target language, especially for low-resource target languages. Language distances are widely used to rank candidate sources due to their correlation with transfer efficacy and applicability in resource-sparse settings. However, the reliability of distance-based rankers across tasks and resource levels remains underexplored. We therefore present the first equity-focused evaluation of paradigms for ranking source languages, studying resource-level inequality and task inequality across ten cross-lingual tasks and two multilingual models. While both inequalities are most pronounced for individual language distances and an English-always baseline, they are substantially reduced by training-free composite distances, and nearly eliminated by trained rankers. We further demonstrate the reliability of rankers using language distances compared to rankers using language model internals. Overall, we find that language distances provide a practical basis for equitable and performant transfer language selection. We recommend using trained rankers when task-specific transfer evaluations are available, and composite distances otherwise.
CommentsAccepted to MRL 2026
