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

• URL: http://arxiv.org/abs/2211.05987v2
• Date: Tue, 13 Feb 2024 02:51:03 GMT
• Title: CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification
• Authors: Yang Li, Canran Xu, Guodong Long, Tao Shen, Chongyang Tao and Jing Jiang
• Abstract summary: We propose a brand-new prefix-tuning method, Counterfactual Contrastive Prefix-tuning (CCPrefix) for many-class classification. Basically, an instance-dependent soft prefix, derived from fact-counterfactual pairs in the label space, is leveraged to complement the language verbalizers in many-class classification.
• Score: 57.62886091828512
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 human-decision process that the most ambiguous classes would be mulled over for each instance, we propose a brand-new prefix-tuning method, Counterfactual Contrastive Prefix-tuning (CCPrefix), for many-class classification. Basically, an instance-dependent soft prefix, derived from fact-counterfactual pairs in the label space, is leveraged to complement the language verbalizers in many-class classification. We conduct experiments on many-class benchmark datasets in both the fully supervised setting and the few-shot setting, which indicates that our model outperforms former baselines.

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