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.

通过引入 LangBridge，我们介绍了一种零 - shot 方法，用于在没有多语言监督的情况下适应语言模型的多语种推理任务。虽然仅利用英语数据进行训练，LangBridge 显着提高了语言模型在数学推理、编码和逻辑推理等低资源语种上的性能。我们分析表明，LangBridge 的有效性来自多语种表示的语言无关特性。我们公开发布了我们的代码和模型。