  It has been shown for English that discrete and soft prompting perform
strongly in few-shot learning with pretrained language models (PLMs). In this
paper, we show that discrete and soft prompting perform better than finetuning
in multilingual cases: Crosslingual transfer and in-language training of
multilingual natural language inference. For example, with 48 English training
examples, finetuning obtains 33.74% accuracy in crosslingual transfer, barely
surpassing the majority baseline (33.33%). In contrast, discrete and soft
prompting outperform finetuning, achieving 36.43% and 38.79%. We also
demonstrate good performance of prompting with training data in multiple
languages other than English.
