Making Pre-trained Language Models Good Long-tailed Learners

• URL: http://arxiv.org/abs/2205.05461v1
• Date: Wed, 11 May 2022 13:03:55 GMT
• Title: Making Pre-trained Language Models Good Long-tailed Learners
• Authors: Chen Zhang, Lei Ren, Jingang Wang, Wei Wu, Dawei Song
• Abstract summary: We check the hypothesis that prompt-tuning is also a promising choice for long-tailed classification. The results demonstrate that prompt-tuning exactly makes pre-trained language models at least good long-tailed learners.
• Score: 14.63635884051461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt-tuning has shown appealing performance in few-shot classification by virtue of its capability in effectively exploiting pre-trained knowledge. This motivates us to check the hypothesis that prompt-tuning is also a promising choice for long-tailed classification, since the tail classes are intuitively few-shot ones. To achieve this aim, we conduct empirical studies to examine the hypothesis. The results demonstrate that prompt-tuning exactly makes pre-trained language models at least good long-tailed learners. For intuitions on why prompt-tuning can achieve good performance in long-tailed classification, we carry out an in-depth analysis by progressively bridging the gap between prompt-tuning and commonly used fine-tuning. The summary is that the classifier structure and parameterization form the key to making good long-tailed learners, in comparison with the less important input structure. Finally, we verify the applicability of our finding to few-shot classification.

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