The remarkable ability of Large Language Models (LLMs) to understand and follow instructions has sometimes been limited by their in-context learning (ICL) performance in low-resource languages. To address this, we introduce a novel approach that leverages cross-lingual retrieval-augmented in-context learning (CREA-ICL). By extracting semantically similar prompts from high-resource languages, we aim to improve the zero-shot performance of multilingual pre-trained language models (MPLMs) across diverse tasks. Though our approach yields steady improvements in classification tasks, it faces challenges in generation tasks. Our evaluation offers insights into the performance dynamics of retrieval-augmented in-context learning across both classification and generation domains.

翻译：大型语言模型（LLMs）在理解和遵循指令方面的卓越能力有时会因低资源语言的情境学习（ICL）性能而受到限制。为解决这一问题，我们提出了一种新颖的方法——跨语言检索增强情境学习（CREA-ICL）。通过从高资源语言中提取语义相似的提示，我们旨在提升多语言预训练语言模型（MPLMs）在多样化任务中的零样本性能。尽管我们的方法在分类任务中取得了稳定的改进，但在生成任务中仍面临挑战。我们的评估为检索增强情境学习在分类与生成领域中的性能动态提供了深入洞察。