Exploring LLM Priming Strategies for Few-Shot Stance Classification

Large language models (LLMs) are effective in predicting the labels of unseen target instances if instructed for the task and training instances via the prompt.LLMs generate a text with higher probability if the prompt contains text with similar characteristics, a phenomenon, called priming, that especially affects argumentation.An open question in NLP is how to systematically exploit priming to choose a set of instances suitable for a given task.For stance classification, LLMs may be primed with fewshot instances prior to identifying whether a given argument is pro or con a topic.In this paper, we explore two priming strategies for fewshot stance classification: one takes those instances that are most semantically similar, and the other chooses those that are most stancesimilar.Experiments on three common stance datasets suggest that priming an LLM with stance-similar instances is particularly effective in few-shot stance classification compared to baseline strategies, and behaves largely consistently across different LLM variants.