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., MetaMath). 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, code completion, logical reasoning, and commonsense 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：本文提出LangBridge，一种无需多语言监督即可使语言模型适应多语言推理任务的零样本方法。LangBridge通过桥接两个分别专精于不同领域的模型实现功能：(1) 专精多语言理解的模型（如mT5编码器），以及(2) 专精推理的模型（如MetaMath）。该方法通过在两个模型间引入极少量可训练参数实现连接。尽管仅使用英文数据进行训练，LangBridge在数学推理、代码补全、逻辑推理和常识推理任务中，显著提升了语言模型在低资源语言上的性能。我们的分析表明，LangBridge的有效性源于多语言表征的语言无关特性。我们将公开代码与模型。