用提示工程让大模型零样本识别讽刺并给出合理解释。
Irony Detection, Reasoning and Understanding in Zero-shot Learning
- 设计IDADP框架生成针对性提示，提升模型泛化能力。
- 在多个数据集上实现零样本讽刺检测，推理过程可读性强。
- 适合研究大模型语义理解与零样本推理的学者参考。
讽刺检测的泛化能力面临重大挑战，导致模型在不同真实场景中表现差异显著。本研究发现，基于我们提出的IDADP框架生成的讽刺相关提示，不仅能突破数据集特定限制，还能生成连贯、人类可读的推理过程，将讽刺文本还原为真实含义。基于研究发现与深入分析，我们提出了若干未来研究方向，包括增强大模型在讽刺检测中的上下文感知能力、探索符号-神经混合方法、整合多模态数据等，以进一步提升其零样本讽刺识别、推理与理解能力。
原文摘要 · Abstract (English)
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.
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