Not All Languages Are Created Equal in LLMs: Improving Multilingual
  Capability by Cross-Lingual-Thought Prompting
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.
645db75ea03f3ebb0be357d8
Haoyang Huang
admin_assigned
2023-06-02T13:00:03.756Z
63455a98ac1cb29fb2a55907
/avatars/1dfa775e2816725faf0133ecc7439c0a.svg
Huang Haoyang
user
Knighhy
645db75ea03f3ebb0be357d9
Tianyi Tang
admin_assigned
2023-06-02T13:01:10.086Z
6293243ee8de908bda81d4db
/avatars/3013edb81a3853401c9e5eb331fe7863.svg
Tianyi Tang
user
StevenTang
645db75ea03f3ebb0be357da
Dongdong Zhang
claimed_verified
2023-05-15T07:34:09.103Z
640809b4656adc27c7733ee0
/avatars/e4f075e4927dae7845d17d396addd3a2.svg
Dongdong Zhang
user
zddfunseeker
645db75ea03f3ebb0be357db
Wayne Xin Zhao
645db75ea03f3ebb0be357dc
Ting Song
645db75ea03f3ebb0be357dd
Yan Xia
645db75ea03f3ebb0be357de
Furu Wei
admin_assigned
2023-06-02T13:02:18.109Z
6368c512fbfe97c16a40baba
/avatars/1c23bc7c0b6d9225699ce27647623d7a.svg
Furu Wei
user
thegenerality
Not All Languages Are Created Equal in LLMs: Improving Multilingual
  Capability by Cross-Lingual-Thought Prompting
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.
Cross-lingual-thought prompting improves multilingual performance and reduces performance gaps across different tasks and languages.
large language models LLMs multilingual capability cross-lingual-thought prompting XLT cross-lingual reasoning logical reasoning arithmetic reasoning open-domain question-answering