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：本研究提出了一种新颖的讽刺检测方法，利用大语言模型结合提示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因依赖静态语言特征和预定义知识库而常显不足，往往忽视了讽刺中至关重要的微妙情感维度。相比之下，我们的方法通过整合经由大语言模型增强的细腻情感线索，将其注入三种基准预训练自然语言处理模型——BERT、T5和GPT-2中，这些模型在讽刺检测领域被广泛认为是基础架构。我们采用SemEval-2018任务3数据集评估了该方法，并观察到讽刺检测能力得到了显著提升。