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Large language models (LLMs) demonstrate impressive multilingual capability,but their performance varies substantially across different languages. In thiswork, we introduce a simple yet effective method, called cross-lingual-thoughtprompting (XLT), to systematically improve the multilingual capability of LLMs.Specifically, XLT is a generic template prompt that stimulates cross-lingualand logical reasoning skills to enhance task performance across languages. Weconduct comprehensive evaluations on 7 typical benchmarks related to reasoning,understanding, and generation tasks, covering both high-resource andlow-resource languages. Experimental results show that XLT not only remarkablyenhances the performance of various multilingual tasks but also significantlyreduces the gap between the average performance and the best performance ofeach task in different languages. Notably, XLT brings over 10 points of averageimprovement in arithmetic reasoning and open-domain question-answering tasks.

Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting