Multilingual large language models (MLLMs) have demonstrated significant cross-lingual capabilities through in-context learning. Existing approaches typically construct monolingual few-shot examples, either in the source or target language. However, translating entire in-context examples into the target language might compromise contextual integrity and be costly in the case of long-context passages. To address this, we introduce Cross-lingual QA, a cross-lingual prompting method that translates only the question and answer parts, thus reducing translation costs. Experiments on four typologically diverse multilingual benchmarks show that Cross-lingual QA prompting effectively stimulates models to elicit their cross-lingual knowledge, outperforming prior monolingual few-shot prompting approaches. Furthermore, we show that prompting open-source MLLMs with cross-lingual few-shot examples enhances performance as the model scale increases.

翻译：多语言大语言模型（MLLMs）已通过上下文学习展现出显著的跨语言能力。现有方法通常构建单语言少样本示例，其语言可为源语言或目标语言。然而，将整个上下文示例翻译为目标语言可能损害上下文完整性，且在处理长上下文段落时成本高昂。为解决此问题，我们提出跨语言问答，一种仅翻译问题与答案部分的跨语言提示方法，从而降低翻译成本。在四个类型多样的多语言基准测试上的实验表明，跨语言问答提示能有效激发模型调用其跨语言知识，性能优于先前的单语言少样本提示方法。此外，我们发现使用跨语言少样本示例提示开源多语言大语言模型时，其性能随模型规模增大而提升。