  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 makes pretrained
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 in-depth analyses by progressively bridging the gap between
prompt-tuning and commonly used finetuning. 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. Good
long-tailed learners can be abbreviated as Glee.
