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展现出非凡能力，但这一能力有时受限于其在低资源语言中的上下文学习（ICL）表现。为此，我们提出了一种新颖方法，利用跨语言检索增强的上下文学习（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）在多种任务上的零样本性能。尽管该方法在分类任务中持续带来改进，但在生成任务中仍面临挑战。我们的评估提供了关于检索增强上下文学习在分类与生成领域性能动态的洞见。