Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models

• URL: http://arxiv.org/abs/2503.18681v2
• Date: Tue, 25 Mar 2025 04:33:15 GMT
• Title: Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models
• Authors: Yazhou Zhang, Chunwang Zou, Bo Wang, Jing Qin,
• Abstract summary: We propose an innovative multi-modal Commander-GPT framework for sarcasm detection.<n>Inspired by military strategy, we first decompose the sarcasm detection task into six distinct sub-tasks.<n>A central commander (decision-maker) then assigns the best-suited large language model to address each specific sub-task.<n>Our approach achieves state-of-the-art performance, with a 19.3% improvement in F1 score.
• Score: 10.47267683821842
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sarcasm detection, as a crucial research direction in the field of Natural Language Processing (NLP), has attracted widespread attention. Traditional sarcasm detection tasks have typically focused on single-modal approaches (e.g., text), but due to the implicit and subtle nature of sarcasm, such methods often fail to yield satisfactory results. In recent years, researchers have shifted the focus of sarcasm detection to multi-modal approaches. However, effectively leveraging multi-modal information to accurately identify sarcastic content remains a challenge that warrants further exploration. Leveraging the powerful integrated processing capabilities of Multi-Modal Large Language Models (MLLMs) for various information sources, we propose an innovative multi-modal Commander-GPT framework. Inspired by military strategy, we first decompose the sarcasm detection task into six distinct sub-tasks. A central commander (decision-maker) then assigns the best-suited large language model to address each specific sub-task. Ultimately, the detection results from each model are aggregated to identify sarcasm. We conducted extensive experiments on MMSD and MMSD 2.0, utilizing four multi-modal large language models and six prompting strategies. Our experiments demonstrate that our approach achieves state-of-the-art performance, with a 19.3% improvement in F1 score, without necessitating fine-tuning or ground-truth rationales.

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