Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discrete and Soft Prompting for Multilingual Models   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.

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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
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The categories of this paper are: physics
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The categories of this paper are: chem-ph
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The categories of this paper are: eess
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga