CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification

Recently, prefix-tuning was proposed to efficiently adapt pre-trained language models to a broad spectrum of natural language classification tasks. It leverages soft prefix as task-specific indicators and language verbalizers as categorical-label mentions to narrow the formulation gap from pre-training language models. However, when the label space increases considerably (i.e., many-class classification), such a tuning technique suffers from a verbalizer ambiguity problem since the many-class labels are represented by semantic-similar verbalizers in short language phrases. To overcome this, inspired by the …

adapt categorical class classification counterfactual cs.cl gap language language models narrow natural natural language pre-training space spectrum tasks training