Not All Languages Are Created Equal in LLMs: Improving Multilingual
  Capability by Cross-Lingual-Thought Prompting
Authors: name: Haoyang Huang, name: Tianyi Tang, name: Dongdong Zhang, name: Wayne Xin Zhao, name: Ting Song, name: Yan Xia, name: Furu Wei

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.