Irony Detection, Reasoning and Understanding in Zero-shot Learning

TL;DR

This paper introduces a novel approach using irony-focused prompts generated by the IDADP framework to improve zero-shot irony detection, reasoning, and understanding in large language models, addressing dataset limitations and enhancing interpretability.

Contribution

The study presents the IDADP framework for generating prompts that enable LLMs to better detect and understand irony in zero-shot settings, with insights into future research directions.

Findings

Irony-focused prompts improve zero-shot irony detection accuracy.

Generated reasoning is coherent and human-readable.

Identifies promising future research directions for irony understanding.

Abstract

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.