MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection
Weihai Lu, Zhejun Zhao, Yanshu Li, Huan He
TLDR
MM-StanceDet is a multi-agent framework that uses retrieval augmentation and structured reasoning to improve multimodal stance detection, outperforming SOTA.
Key contributions
- Integrates Retrieval Augmentation for better contextual grounding in multimodal stance detection.
- Employs specialized Multimodal Analysis agents for nuanced cross-modal interpretation.
- Features a Reasoning-Enhanced Debate stage to explore diverse perspectives and resolve ambiguities.
- Incorporates Self-Reflection for robust adjudication and improved overall performance.
Why it matters
MM-StanceDet significantly advances multimodal stance detection by introducing a robust multi-agent framework. It addresses key challenges like contextual grounding and cross-modal ambiguity, offering a more reliable approach to understanding public discourse. This structured reasoning model sets a new standard for complex AI tasks.
Original Abstract
Multimodal Stance Detection (MSD) is crucial for understanding public discourse, yet effectively fusing text and image, especially with conflicting signals, remains challenging. Existing methods often face difficulties with contextual grounding, cross-modal interpretation ambiguity, and single-pass reasoning fragility. To address these, we propose Retrieval-Augmented Multi-modal Multi-agent Stance Detection (MM-StanceDet), a novel multi-agent framework integrating Retrieval Augmentation for contextual grounding, specialized Multimodal Analysis agents for nuanced interpretation, a Reasoning-Enhanced Debate stage for exploring perspectives, and Self-Reflection for robust adjudication. Extensive experiments on five datasets demonstrate MM-StanceDet significantly outperforms state-of-the-art baselines, validating the efficacy of its multi-agent architecture and structured reasoning stages in addressing complex multimodal stance challenges.
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