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- W3213404405 abstract "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." @default.
- W3213404405 created "2021-11-22" @default.
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- W3213404405 creator A5071144367 @default.
- W3213404405 date "2021-09-08" @default.
- W3213404405 modified "2023-09-26" @default.
- W3213404405 title "Discrete and Soft Prompting for Multilingual Models" @default.
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- W3213404405 doi "https://doi.org/10.48550/arxiv.2109.03630" @default.
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