Irony Detection, Reasoning and Understanding in Zero-shot Learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913888864305152 |
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| author | Yi, Peiling Xia, Yuhan Long, Yunfei |
| author_facet | Yi, Peiling Xia, Yuhan Long, Yunfei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16884 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Irony Detection, Reasoning and Understanding in Zero-shot Learning Yi, Peiling Xia, Yuhan Long, Yunfei Computation and Language Artificial Intelligence 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. |
| title | Irony Detection, Reasoning and Understanding in Zero-shot Learning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2501.16884 |