跨语言问答：解锁上下文跨语言性能的关键
Cross-lingual QA: A Key to Unlocking In-context Cross-lingual Performance
AI总结：
提出跨语言问答提示法，仅翻译问答部分以降低成本，在四个多语言基准上优于单语提示，并随模型规模增大性能提升。
摘要：
多语言大语言模型（MLLMs）通过上下文学习展现了显著的跨语言能力。现有方法通常构建单语上下文示例，要么使用源语言，要么使用目标语言。然而，将整个上下文示例翻译成目标语言可能会损害上下文完整性，并且在长上下文段落的情况下成本高昂。为解决此问题，我们引入了跨语言问答（Cross-lingual QA），一种仅翻译问题和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上的实验表明，跨语言问答提示能有效激发模型调动其跨语言知识，优于先前的单语提示方法。此外，我们表明，随着模型规模的增加，使用跨语言上下文示例提示开源MLLMs可提升性能。
英文摘要：
Multilingual large language models (MLLMs) have demonstrated significant cross-lingual capabilities through in-context learning. Existing approaches typically construct monolingual in-context 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 prompting approaches. Furthermore, we show that prompting open-source MLLMs with cross-lingual in-context examples enhances performance as the model scale increases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
