IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection
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| Format: | Preprint |
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2025
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| _version_ | 1866908499812810752 |
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| author | Ramakrishnan, Aashish Anantha Ramakrishnan, Aadarsh Anantha Lee, Dongwon |
| author_facet | Ramakrishnan, Aashish Anantha Ramakrishnan, Aadarsh Anantha Lee, Dongwon |
| contents | Interpreting figurative language such as sarcasm across multi-modal inputs presents unique challenges, often requiring task-specific fine-tuning and extensive reasoning steps. However, current Chain-of-Thought approaches do not efficiently leverage the same cognitive processes that enable humans to identify sarcasm. We present IRONIC, an in-context learning framework that leverages Multi-modal Coherence Relations to analyze referential, analogical and pragmatic image-text linkages. Our experiments show that IRONIC achieves state-of-the-art performance on zero-shot Multi-modal Sarcasm Detection across different baselines. This demonstrates the need for incorporating linguistic and cognitive insights into the design of multi-modal reasoning strategies. Our code is available at: https://github.com/aashish2000/IRONIC |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_16258 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection Ramakrishnan, Aashish Anantha Ramakrishnan, Aadarsh Anantha Lee, Dongwon Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition 68T50 I.2.7; I.2.10 Interpreting figurative language such as sarcasm across multi-modal inputs presents unique challenges, often requiring task-specific fine-tuning and extensive reasoning steps. However, current Chain-of-Thought approaches do not efficiently leverage the same cognitive processes that enable humans to identify sarcasm. We present IRONIC, an in-context learning framework that leverages Multi-modal Coherence Relations to analyze referential, analogical and pragmatic image-text linkages. Our experiments show that IRONIC achieves state-of-the-art performance on zero-shot Multi-modal Sarcasm Detection across different baselines. This demonstrates the need for incorporating linguistic and cognitive insights into the design of multi-modal reasoning strategies. Our code is available at: https://github.com/aashish2000/IRONIC |
| title | IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition 68T50 I.2.7; I.2.10 |
| url | https://arxiv.org/abs/2505.16258 |