IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection
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
Code (0)
등록된 구현이 없습니다.
Tasks
In-Context LearningSarcasm DetectionSimilar Papers 제목 키워드 기반
Enhancing Text Generation with Inductive Event Reasoning
How to generate informative, coherent natural language is a very important task. Previous studies mainly focus on leveraging commonsense knowledge into generative models, which can improve the informativeness of generate…
InformativenessText GenerationShort Chains, Deep Thoughts: Balancing Reasoning Efficiency and Intra-Segment Capability via Split-Merge Optimization
While Large Reasoning Models (LRMs) have demonstrated impressive capabilities in solving complex tasks through the generation of long reasoning chains, this reliance on verbose generation results in significant latency a…
Reinforcement LearningUnderstanding Chain-of-Thought in Large Language Models via Topological Data Analysis
With the development of large language models (LLMs), particularly with the introduction of the long reasoning chain technique, the reasoning ability of LLMs in complex problem-solving has been significantly enhanced. Wh…
DeepEdit: Knowledge Editing as Decoding with Constraints
How to edit the knowledge in multi-step reasoning has become the major challenge in the knowledge editing (KE) of large language models (LLMs). The difficulty arises because the hallucinations of LLMs during multi-step r…
Informativenessknowledge editingMulti-hop Question AnsweringQuestion AnsweringExploiting Reasoning Chains for Multi-hop Science Question Answering
We propose a novel Chain Guided Retriever-reader ({\tt CGR}) framework to model the reasoning chain for multi-hop Science Question Answering. Our framework is capable of performing explainable reasoning without the need …
Abstract Meaning RepresentationARCQuestion AnsweringScience Question Answering