Development of large language models (LLM) have shown progress on reasoning,
though studies have been limited to English or simple reasoning tasks. We thus
introduce a multilingual structured reasoning and explanation dataset, termed
xSTREET, that covers four tasks across six languages. xSTREET exposes a gap in
base LLM performance between English and non-English reasoning tasks. We then
propose two methods to remedy this gap, building on the insight that LLMs
trained on code are better reasoners. First, at training time, we augment a
code dataset with multi-lingual comments using machine translation while
keeping program code as-is. Second, at inference time, we bridge the gap
between training and inference by employing a prompt structure that
incorporates step-by-step code primitives to derive new facts and find a
solution. Our methods show improved multilingual performance on xSTREET, most
notably on the scientific commonsense reasoning subtask. Furthermore, the
models show no regression on non-reasoning tasks, thus showing our techniques
maintain general-purpose abilities.

大型语言模型在多语言结构推理和解释数据集 xSTREET 上表现出进展，通过使用机器翻译增强具有多语言评论的代码数据集，以及在推理时使用包含逐步代码原语的提示结构，提供了改善多语言性能的两种方法。