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With the recent rise of large language models (LLMs), in-context learning (ICL) has shown remarkable performance, eliminating the need for fine-tuning parameters and reducing the reliance on extensive labeled data. However, the intricacies of cross-lingual ICL remain underexplored. Prior studies on cross-lingual ICL overlooked the significance of language-specific nuances, neglecting the intrinsic linguistic properties of sentences and the interlingual connections between sentences in different languages. In this paper, we propose a novel cross-lingual prompt structure: Language-Emphasized cross-lingual In-context learning (LEI). LEI teaches LLMs how to adapt to language conversion by adding explicit language conversion examples in demonstrations. Specifically, LEI introduces a third language (example language) as an example of language conversion to adapt LLMs to language conversion in cross-lingual tasks. In addition, language alignment of demonstrations is achieved by adding language aligners and label aligners. Extensive experiments validate the state-of-the-art performance of LEI on 42 cross-lingual tasks.

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Language-Emphasized Cross-Lingual In-Context Learning for Multilingual LLM

  • 会议论文

  • 发表日期：

    2025年

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  • Junlei Li,
  • Xiao Wei,
  • Xiaobao Wang,
  • Ning Zhuang,
  • Longbiao Wang,
  • Jianwu Dang

摘要

With the recent rise of large language models (LLMs), in-context learning (ICL) has shown remarkable performance, eliminating the need for fine-tuning parameters and reducing the reliance on extensive labeled data. However, the intricacies of cross-lingual ICL remain underexplored. Prior studies on cross-lingual ICL overlooked the significance of language-specific nuances, neglecting the intrinsic linguistic properties of sentences and the interlingual connections between sentences in different languages. In this paper, we propose a novel cross-lingual prompt structure: Language-Emphasized cross-lingual In-context learning (LEI). LEI teaches LLMs how to adapt to language conversion by adding explicit language conversion examples in demonstrations. Specifically, LEI introduces a third language (example language) as an example of language conversion to adapt LLMs to language conversion in cross-lingual tasks. In addition, language alignment of demonstrations is achieved by adding language aligners and label aligners. Extensive experiments validate the state-of-the-art performance of LEI on 42 cross-lingual tasks.