The generalisation of irony detection faces significant challenges, leading to substantial performance deviations when detection models are applied to diverse real-world scenarios. In this study, we find that irony-focused prompts, as generated from our IDADP framework for LLMs, can not only overcome dataset-specific limitations but also generate coherent, human-readable reasoning, transforming ironic text into its intended meaning. Based on our findings and in-depth analysis, we identify several promising directions for future research aimed at enhancing LLMs' zero-shot capabilities in irony detection, reasoning, and comprehension. These include advancing contextual awareness in irony detection, exploring hybrid symbolic-neural methods, and integrating multimodal data, among others.
@article{arxiv.2501.16884,
title = {Irony Detection, Reasoning and Understanding in Zero-shot Learning},
author = {Peiling Yi and Yuhan Xia and Yunfei Long},
journal= {arXiv preprint arXiv:2501.16884},
year = {2025}
}