Zero-Shot Cross-lingual Transfer (ZS-XLT) utilizes a model trained in a
source language to make predictions in another language, often with a
performance loss. To alleviate this, additional improvements can be achieved
through subsequent adaptation using examples in the target language. In this
paper, we exploit In-Context Tuning (ICT) for One-Shot Cross-lingual transfer
in the classification task by introducing In-Context Cross-lingual Transfer
(IC-XLT). The novel concept involves training a model to learn from context
examples and subsequently adapting it during inference to a target language by
prepending a One-Shot context demonstration in that language. Our results show
that IC-XLT successfully leverages target-language examples to improve the
cross-lingual capabilities of the evaluated mT5 model, outperforming
prompt-based models in the Zero and Few-shot scenarios adapted through
fine-tuning. Moreover, we show that when source-language data is limited, the
fine-tuning framework employed for IC-XLT performs comparably to prompt-based
fine-tuning with significantly more training data in the source language.

通过在分类任务中介绍上下文一次性跨语言转换（IC-XLT），可以通过训练模型学习上下文示例，然后在推断时通过在目标语言中预置一次性上下文演示来适应目标语言，提高评估的 mT5 模型的跨语言能力，超过基于提示的模型在经过微调的零和少样本情景中的表现。此外，当源语言数据有限时，IC-XLT 的微调框架与源语言中具有显著更多训练数据的基于提示的微调具有相当的性能。