  Large language models (LLMs) are known to effectively perform tasks by simply
observing few exemplars. However, in low-resource languages, obtaining such
hand-picked exemplars can still be challenging, where unsupervised techniques
may be necessary. Moreover, competent generative capabilities of LLMs are
observed only in high-resource languages, while their performances among
under-represented languages fall behind due to pre-training data imbalance. To
elicit LLMs' ability onto low-resource languages without any supervised data,
we propose to assemble synthetic exemplars from a diverse set of high-resource
languages to prompt the LLMs to translate from any language into English. These
prompts are then used to create intra-lingual exemplars to perform tasks in the
target languages. Our unsupervised prompting method performs on par with
supervised few-shot learning in LLMs of different sizes for translations
between English and 13 Indic and 21 African low-resource languages. We also
show that fine-tuning a 7B model on data generated from our method helps it
perform competitively with a 175B model. In non-English translation tasks, our
method even outperforms supervised prompting by up to 3 chrF++ in many
low-resource languages. When evaluated on zero-shot multilingual summarization,
our method surpasses other English-pivoting baselines by up to 4 ROUGE-L and is
also favored by GPT-4.
