Protum: A New Method For Prompt Tuning Based on "[MASK]"

• URL: http://arxiv.org/abs/2201.12109v1
• Date: Fri, 28 Jan 2022 13:34:30 GMT
• Title: Protum: A New Method For Prompt Tuning Based on "[MASK]"
• Authors: Pan He and Yuxi Chen and Yan Wang and Yanru Zhang
• Abstract summary: We propose a new textbfPrompt textbfTuning based on "[textbfMASK]" (textbfProtum) method in this paper. Our textbfProtum can achieve much better performance than fine-tuning after continuous pre-training with less time consumption.
• Score: 12.057434751507552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, prompt tuning \cite{lester2021power} has gradually become a new paradigm for NLP, which only depends on the representation of the words by freezing the parameters of pre-trained language models (PLMs) to obtain remarkable performance on downstream tasks. It maintains the consistency of Masked Language Model (MLM) \cite{devlin2018bert} task in the process of pre-training, and avoids some issues that may happened during fine-tuning. Naturally, we consider that the "[MASK]" tokens carry more useful information than other tokens because the model combines with context to predict the masked tokens. Among the current prompt tuning methods, there will be a serious problem of random composition of the answer tokens in prediction when they predict multiple words so that they have to map tokens to labels with the help verbalizer. In response to the above issue, we propose a new \textbf{Pro}mpt \textbf{Tu}ning based on "[\textbf{M}ASK]" (\textbf{Protum}) method in this paper, which constructs a classification task through the information carried by the hidden layer of "[MASK]" tokens and then predicts the labels directly rather than the answer tokens. At the same time, we explore how different hidden layers under "[MASK]" impact on our classification model on many different data sets. Finally, we find that our \textbf{Protum} can achieve much better performance than fine-tuning after continuous pre-training with less time consumption. Our model facilitates the practical application of large models in NLP.

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