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LINK：词汇干预实现低资源语言知识迁移
原标题：Multilingual Knowledge Transfer under Data Constraints via Lexical Interventions
AI 摘要
Apple 研究团队提出 LINK 方法，通过在预训练数据中进行词汇替换，实现低资源语言的知识迁移，无需额外训练或平行数据。实验显示在八种语言上显著提升下游任务性能，训练速度最高提升 2 倍。
以上摘要由 AI 生成，可能存在误差。事实请以原文为准。
正文节选
Cross-lingual knowledge transfer is critical for building high-performing multilingual language models for languages with insufficient training data. When target language data is scarce, the knowledge required for many downstream tasks involving scientific reasoning, commonsense inference, and world knowledge must be acquired primarily from the high-resource language, making effective knowledge transfer essential. Existing methods for improving such cross-lingual knowledge transfer require large
发布时间：2026-08-20 08:00
抓取时间：2026-08-20 23:34
来源机构：Apple