Instruction tuning (IT) is widely used to teach pretrained large language
models (LLMs) to follow arbitrary instructions, but is under-studied in
multilingual settings. In this work, we conduct a systematic study of zero-shot
cross-lingual transfer in IT, when an LLM is instruction-tuned on English-only
data and then tested on user prompts in other languages. We investigate the
influence of model configuration choices and devise a multi-facet evaluation
strategy for multilingual instruction following. We find that cross-lingual
transfer does happen successfully in IT even if all stages of model training
are English-centric, but only if multiliguality is taken into account in
hyperparameter tuning and with large enough IT data. English-trained LLMs are
capable of generating correct-language, comprehensive and helpful responses in
the other languages, but suffer from low factuality and may occasionally have
fluency errors.

在多语种环境下，我们对指令调优进行了系统研究，发现跨语言转移成功的关键在于超参数调整和足够的训练数据，虽然英文训练的大型语言模型能够生成其他语言的正确、全面和有帮助的回答，但其可信度较低且可能偶尔出现流畅性错误。