Large language models show compelling performance on reasoning tasks but they
tend to perform much worse in languages other than English. This is
unsurprising given that their training data largely consists of English text
and instructions. A typical solution is to translate instruction data into all
languages of interest, and then train on the resulting multilingual data, which
is called translate-training. This approach not only incurs high cost, but also
results in poorly translated data due to the non-standard formatting of
chain-of-thought and mathematical reasoning instructions. In this paper, we
explore the benefits of question alignment, where we train the model to
translate reasoning questions into English by finetuning on X-English question
data. In this way we perform targetted, in-domain language alignment which
makes best use of English instruction data to unlock the LLMs' multilingual
reasoning abilities. Experimental results on LLaMA2-13B show that question
alignment leads to consistent improvements over the translate-training
approach: an average improvement of 11.3\% and 16.1\% accuracy across ten
languages on the MGSM and MSVAMP maths reasoning benchmarks (The project will
be available at: this https URL).

通过问题对齐，我们训练模型将推理问题翻译成英文，以利用英文指导数据，提高大型语言模型在多语言推理任务上的性能。