Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. In this work, we introduce a simple yet effective method, called cross-lingual-thought prompting (XLT), to systematically improve the multilingual capability of LLMs. Specifically, XLT is a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages. We conduct comprehensive evaluations on 7 typical benchmarks related to reasoning, understanding, and generation tasks, covering both high-resource and low-resource languages. Experimental results show that XLT not only remarkably enhances the performance of various multilingual tasks but also significantly reduces the gap between the average performance and the best performance of each task in different languages. Notably, XLT brings over 10 points of average improvement in arithmetic reasoning and open-domain question-answering tasks.

翻译：大语言模型（LLMs）展现出令人印象深刻的多语言能力，但其在不同语言间的性能差异显著。本文提出一种简单而有效的方法——跨语言思维提示（XLT），旨在系统性提升LLMs的多语言能力。具体而言，XLT是一种通用模板提示，通过激发跨语言与逻辑推理能力来增强各类任务的语言表现。我们在推理、理解和生成任务相关的7个典型基准上进行了全面评估，覆盖高资源语言与低资源语言。实验结果表明，XLT不仅显著提升了多种多语言任务的性能，还大幅缩小了各任务在不同语言中平均性能与最佳性能之间的差距。值得注意的是，在算术推理与开放域问答任务中，XLT带来了超过10个百分点的平均性能提升。