Evolutionary Verbalizer Search for Prompt-based Few Shot Text Classification

• URL: http://arxiv.org/abs/2306.10514v1
• Date: Sun, 18 Jun 2023 10:03:11 GMT
• Title: Evolutionary Verbalizer Search for Prompt-based Few Shot Text Classification
• Authors: Tongtao Ling, Lei Chen, Yutao Lai and Hai-Lin Liu
• Abstract summary: We propose a novel evolutionary verbalizer search (EVS) algorithm to improve prompt-based tuning with the high-performance verbalizer. In this paper, we focus on automatically constructing the optimal verbalizer and propose a novelEVS algorithm to improve prompt-based tuning with the high-performance verbalizer.
• Score: 5.583948835737293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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