SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific Topics

TL;DR

SciPrompt is a framework that automatically enriches verbalizers with scientific terms to improve prompt-based classification of scientific topics, especially in low-resource scenarios.

Contribution

It introduces an automatic retrieval method for domain-specific terms and a weighted verbalization strategy, enhancing prompt-based fine-tuning for scientific text classification.

Findings

Outperforms state-of-the-art prompt-based methods in scientific classification

Effective in few-shot and zero-shot settings

Excels in classifying fine-grained and emerging topics

Abstract

Prompt-based fine-tuning has become an essential method for eliciting information encoded in pre-trained language models for a variety of tasks, including text classification. For multi-class classification tasks, prompt-based fine-tuning under low-resource scenarios has resulted in performance levels comparable to those of fully fine-tuning methods. Previous studies have used crafted prompt templates and verbalizers, mapping from the label terms space to the class space, to solve the classification problem as a masked language modeling task. However, cross-domain and fine-grained prompt-based fine-tuning with an automatically enriched verbalizer remains unexplored, mainly due to the difficulty and costs of manually selecting domain label terms for the verbalizer, which requires humans with domain expertise. To address this challenge, we introduce SciPrompt, a framework designed to automatically retrieve scientific topic-related terms for low-resource text classification tasks. To this end, we select semantically correlated and domain-specific label terms within the context of scientific literature for verbalizer augmentation. Furthermore, we propose a new verbalization strategy that uses correlation scores as additional weights to enhance the prediction performance of the language model during model tuning. Our method outperforms state-of-the-art, prompt-based fine-tuning methods on scientific text classification tasks under few and zero-shot settings, especially in classifying fine-grained and emerging scientific topics.