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In-context learning (ICL) unfolds as large language models become capable ofinferring test labels conditioned on a few labeled samples without any gradientupdate. ICL-enabled large language models provide a promising step forwardtoward bypassing recurrent annotation costs in a low-resource setting. Yet,only a handful of past studies have explored ICL in a cross-lingual setting, inwhich the need for transferring label-knowledge from a high-resource languageto a low-resource one is immensely crucial. To bridge the gap, we provide thefirst in-depth analysis of ICL for cross-lingual text classification. We findthat the prevalent mode of selecting random input-label pairs to construct theprompt-context is severely limited in the case of cross-lingual ICL, primarilydue to the lack of alignment in the input as well as the output spaces. Tomitigate this, we propose a novel prompt construction strategy -- Cross-lingualIn-context Source-Target Alignment (X-InSTA). With an injected coherence in thesemantics of the input examples and a task-based alignment across the sourceand target languages, X-InSTA is able to outperform random prompt selection bya large margin across three different tasks using 44 different cross-lingualpairs.

Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment