Despite the rapid recent progress in creating accurate and compact in-context
learners, most recent work focuses on in-context learning (ICL) for tasks in
English. However, the ability to interact with users of languages outside
English presents a great potential for broadening the applicability of language
technologies to non-English speakers.
In this work, we collect the infrastructure necessary for training and
evaluation of ICL in a selection of Slavic languages: Czech, Polish, and
Russian. We link a diverse set of datasets and cast these into a unified
instructional format through a set of transformations and newly-crafted
templates written purely in target languages. Using the newly-curated dataset,
we evaluate a set of the most recent in-context learners and compare their
results to the supervised baselines. Finally, we train, evaluate and publish a
set of in-context learning models that we train on the collected resources and
compare their performance to previous work.
We find that ICL models tuned in English are also able to learn some tasks
from non-English contexts, but multilingual instruction fine-tuning
consistently improves the ICL ability. We also find that the massive multitask
training can be outperformed by single-task training in the target language,
uncovering the potential for specializing in-context learners to the
language(s) of their application.

本研究收集了在斯拉夫语言（捷克语、波兰语和俄语）上进行 ICL 培训和评估的基础设施，并比较了使用新收集的数据集训练的 ICL 模型与以前的工作的性能，发现多语言指导的微调持续提高了 ICL 的能力，并揭示了将特定语言上下文学习器专门用于其应用语言的潜力。