We introduce LangBridge, a zero-shot approach to adapt language models for multilingual reasoning tasks without multilingual supervision. LangBridge operates by bridging two models, each specialized in different aspects: (1) one specialized in understanding multiple languages (e.g., mT5 encoder) and (2) one specialized in reasoning (e.g., Orca 2). LangBridge connects the two models by introducing minimal trainable parameters between them. Despite utilizing only English data for training, LangBridge considerably enhances the performance of language models on low-resource languages across mathematical reasoning, coding, and logical reasoning. Our analysis suggests that the efficacy of LangBridge stems from the language-agnostic characteristics of multilingual representations. We publicly release our code and models.

翻译：我们提出LangBridge，一种无需多语言监督即可将语言模型适配于多语言推理任务的零样本方法。LangBridge通过桥接两个分别专注不同特长的模型来运作：（1）一个模型擅长理解多种语言（例如mT5编码器），（2）另一个模型擅长推理（例如Orca 2）。LangBridge通过在两者之间引入极少量可训练参数来连接这两个模型。尽管仅使用英语数据进行训练，LangBridge在数学推理、编程和逻辑推理任务中显著提升了语言模型在低资源语言上的表现。我们的分析表明，LangBridge的有效性源于多语言表示的语言无关特性。我们已公开发布代码和模型。