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arXiv · 2203.03235

Pre-trained Token-replaced Detection Model as Few-shot Learner

Zicheng Li·Shoushan Li·Guodong Zhou
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Abstract

Pre-trained masked language models have demonstrated remarkable ability as few-shot learners. In this paper, as an alternative, we propose a novel approach to few-shot learning with pre-trained token-replaced detection models like ELECTRA. In this approach, we reformulate a classification or a regression task as a token-replaced detection problem. Specifically, we first define a template and label description words for each task and put them into the input to form a natural language prompt. Then, we employ the pre-trained token-replaced detection model to predict which label description word is the most original (i.e., least replaced) among all label description words in the prompt. A systematic evaluation on 16 datasets demonstrates that our approach outperforms few-shot learners with pre-trained masked language models in both one-sentence and two-sentence learning tasks.

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Zicheng Li, Shoushan Li, Guodong Zhou. 2022-03-07. Pre-trained Token-replaced Detection Model as Few-shot Learner. https://arxiv.org/abs/2203.03235

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