title: Not All Languages Are Created Equal in LLMs: Improving Multilingual
    Capability by Cross-Lingual-Thought Prompting
summary: 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.
paper_authors._id: 645db75ea03f3ebb0be357d8
paper_authors.name: Haoyang Huang
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/1dfa775e2816725faf0133ecc7439c0a.svg
paper_authors.fullname: Huang Haoyang
paper_authors.type: user
paper_authors.user: Knighhy
paper_authors._id: 645db75ea03f3ebb0be357d9
paper_authors.name: Tianyi Tang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T13:01:10.086Z
paper_authors._id: 6293243ee8de908bda81d4db
paper_authors.avatarUrl: /avatars/3013edb81a3853401c9e5eb331fe7863.svg
paper_authors.fullname: Tianyi Tang
paper_authors.type: user
paper_authors.user: StevenTang
paper_authors._id: 645db75ea03f3ebb0be357da
paper_authors.name: Dongdong Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-15T07:34:09.103Z
paper_authors._id: 640809b4656adc27c7733ee0
paper_authors.avatarUrl: /avatars/e4f075e4927dae7845d17d396addd3a2.svg
paper_authors.fullname: Dongdong Zhang
paper_authors.type: user
paper_authors.user: zddfunseeker
paper_authors._id: 645db75ea03f3ebb0be357db
paper_authors.name: Wayne Xin Zhao
paper_authors._id: 645db75ea03f3ebb0be357dc
paper_authors.name: Ting Song
paper_authors._id: 645db75ea03f3ebb0be357dd
paper_authors.name: Yan Xia
paper_authors._id: 645db75ea03f3ebb0be357de
paper_authors.name: Furu Wei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T13:02:18.109Z
paper_authors._id: 6368c512fbfe97c16a40baba
paper_authors.avatarUrl: /avatars/1c23bc7c0b6d9225699ce27647623d7a.svg
paper_authors.fullname: Furu Wei
paper_authors.type: user
paper_authors.user: thegenerality
paper_title: Not All Languages Are Created Equal in LLMs: Improving Multilingual
    Capability by Cross-Lingual-Thought Prompting
paper_summary: 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.
paper_ai_summary: Cross-lingual-thought prompting improves multilingual performance and reduces performance gaps across different tasks and languages.
paper_ai_keywords: large language models LLMs multilingual capability cross-lingual-thought prompting XLT cross-lingual reasoning logical reasoning arithmetic reasoning open-domain question-answering