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    <abstract>Pre-trained masked language models successfully perform few-shot learning by formulating downstream tasks as text infilling. How- ever, as a strong alternative in full-shot settings, discriminative pre-trained models like ELECTRA do not fit into the paradigm. In this work, we adapt prompt-based few-shot learning to ELECTRA and show that it outperforms masked language models in a wide range of tasks. ELECTRA is pre-trained to distinguish if a token is generated or original. We naturally extend that to prompt-based few-shot learning by training to score the originality of the target options without introducing new parameters. Our method can be easily adapted to tasks involving multi-token predictions without extra computation overhead. Analysis shows that ELECTRA learns distributions that align better with downstream tasks.</abstract>
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%0 Conference Proceedings
%T Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models
%A Xia, Mengzhou
%A Artetxe, Mikel
%A Du, Jingfei
%A Chen, Danqi
%A Stoyanov, Veselin
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F xia-etal-2022-prompting
%X Pre-trained masked language models successfully perform few-shot learning by formulating downstream tasks as text infilling. How- ever, as a strong alternative in full-shot settings, discriminative pre-trained models like ELECTRA do not fit into the paradigm. In this work, we adapt prompt-based few-shot learning to ELECTRA and show that it outperforms masked language models in a wide range of tasks. ELECTRA is pre-trained to distinguish if a token is generated or original. We naturally extend that to prompt-based few-shot learning by training to score the originality of the target options without introducing new parameters. Our method can be easily adapted to tasks involving multi-token predictions without extra computation overhead. Analysis shows that ELECTRA learns distributions that align better with downstream tasks.
%R 10.18653/v1/2022.emnlp-main.780
%U https://aclanthology.org/2022.emnlp-main.780
%U https://doi.org/10.18653/v1/2022.emnlp-main.780
%P 11351-11361

Markdown (Informal)

[Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models](https://aclanthology.org/2022.emnlp-main.780) (Xia et al., EMNLP 2022)

• Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models (Xia et al., EMNLP 2022)

ACL

• Mengzhou Xia, Mikel Artetxe, Jingfei Du, Danqi Chen, and Veselin Stoyanov. 2022. Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11351–11361, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.