  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
mathematical chain-of-thought. 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 parallel question data. In this way we
perform targeted, 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 multilingual reasoning benchmarks. The project will be available at:
https://github.com/NJUNLP/QAlign.
