This study introduces a novel method for irony detection, applying Large Language Models (LLMs) with prompt-based learning to facilitate emotion-centric text augmentation. Traditional irony detection techniques typically fall short due to their reliance on static linguistic features and predefined knowledge bases, often overlooking the nuanced emotional dimensions integral to irony. In contrast, our methodology augments the detection process by integrating subtle emotional cues, augmented through LLMs, into three benchmark pre-trained NLP models - BERT, T5, and GPT-2 - which are widely recognized as foundational in irony detection. We assessed our method using the SemEval-2018 Task 3 dataset and observed substantial enhancements in irony detection capabilities.

翻译：本研究提出了一种讽刺检测的新方法，通过应用大语言模型（LLMs）并采用基于提示的学习来促进以情感为中心的文本增强。传统的讽刺检测技术通常因依赖静态语言特征和预定义知识库而表现不足，往往忽视了讽刺中不可或缺的细腻情感维度。相比之下，我们的方法通过将经LLMs增强的微妙情感线索整合到三个基准预训练NLP模型（即BERT、T5和GPT-2，这些模型被广泛认为是讽刺检测的基础模型）中，从而增强了检测过程。我们利用SemEval-2018任务3数据集评估了该方法，观察到讽刺检测能力得到了显著提升。