Recent advances for few-shot text classification aim to wrap textual inputs with task-specific prompts to cloze questions. By processing them with a masked language model to predict the masked tokens and using a verbalizer that constructs the mapping between predicted words and target labels. This approach of using pre-trained language models is called prompt-based tuning, which could remarkably outperform conventional fine-tuning approach in the low-data scenario. As the core of prompt-based tuning, the verbalizer is usually handcrafted with human efforts or suboptimally searched by gradient descent. In this paper, we focus on automatically constructing the optimal verbalizer and propose a novel evolutionary verbalizer search (EVS) algorithm, to improve prompt-based tuning with the high-performance verbalizer. Specifically, inspired by evolutionary algorithm (EA), we utilize it to automatically evolve various verbalizers during the evolutionary procedure and select the best one after several iterations. Extensive few-shot experiments on five text classification datasets show the effectiveness of our method.

本文提出了一种新的进化式口头表述搜索算法用于构建最优的口头表述器，在少量数据情况下使用预训练的语言模型以及任务特定提示能够极大地提高准确性，进一步推进了小样本文本分类的研究。

基于提示的少样本文本分类的进化语言生成搜索