In-context learning (ICL) empowers large language models (LLMs) to perform
diverse tasks in underrepresented languages using only short in-context
information, offering a crucial avenue for narrowing the gap between
high-resource and low-resource languages. Nonetheless, there is only a handful
of works explored ICL for low-resource languages with most of them focusing on
relatively high-resource languages, such as French and Spanish. In this work,
we extensively study ICL and its cross-lingual variation (X-ICL) on 25
low-resource and 7 relatively higher-resource languages. Our study not only
assesses the effectiveness of ICL with LLMs in low-resource languages but also
identifies the shortcomings of in-context label alignment, and introduces a
more effective alternative: query alignment. Moreover, we provide valuable
insights into various facets of ICL for low-resource languages. Our study
concludes the significance of few-shot in-context information on enhancing the
low-resource understanding quality of LLMs through semantically relevant
information by closing the language gap in the target language and aligning the
semantics between the targeted low-resource and the high-resource language that
the model is proficient in. Our work highlights the importance of advancing ICL
research, particularly for low-resource languages.

通过询问对齐方式的另一种选择，全面研究了一致性学习（ICL）及其跨语言变体（X-ICL）对于 25 种低资源和 7 种相对较高资源的语言的有效性，发现了标签对齐的不足，并提供了有价值的洞察力，强调了提升低资源语言的理解能力的重要性。