  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.
