Overcoming Data Scarcity: Guiding Citation Function Classification with Prompt-Based Few-Shot Learning

Abstract

Citation function analysis is crucial for understanding how cited literature affects the narrative in scientific publications, as citations serve multiple purposes that need accurate distinction and classification. The field faces challenges due to insufficient labeled data and the complexity of defining and categorizing citation functions, requiring specialized knowledge and a deep understanding of scholarly literature. This limitation leads to the imprecise identification and classification of citation functions. To mitigate this challenge, we propose a meta-learning strategy that utilizes prompt learning with pre-trained language models, also known as prompt-based tuning, for the task of few-shot learning in citation function classification. Our findings demonstrate that prompt-based tuning with SciBERT surpasses state-of-the-art pre-trained language models with the conventional fine-tuning approach when labeled data is scarce. Furthermore, we present an analysis that sheds light on the impact of template selection on the prompt-based tuning methodology.