MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection
📰 ArXiv cs.AI
Learn to detect stance in multimodal data using MM-StanceDet, a retrieval-augmented multi-agent approach, to improve public discourse understanding
Action Steps
- Implement MM-StanceDet using a multi-agent framework to fuse text and image modalities
- Use retrieval-augmentation to improve contextual grounding and reduce cross-modal interpretation ambiguity
- Apply MM-StanceDet to a dataset of multimodal posts to detect stance and evaluate its performance
- Compare the results of MM-StanceDet with existing single-pass reasoning methods to assess its advantages
- Fine-tune the MM-StanceDet model using a retrieval-augmented approach to improve its robustness and accuracy
Who Needs to Know This
Researchers and developers in AI and NLP can benefit from this approach to enhance their multimodal stance detection models, while data scientists and analysts can apply this technique to better understand public discourse
Key Insight
💡 MM-StanceDet addresses the challenges of contextual grounding, cross-modal interpretation ambiguity, and single-pass reasoning fragility in multimodal stance detection
Share This
🚀 Introducing MM-StanceDet: a novel retrieval-augmented multi-agent approach for multimodal stance detection! 🤖
Key Takeaways
Learn to detect stance in multimodal data using MM-StanceDet, a retrieval-augmented multi-agent approach, to improve public discourse understanding
Full Article
Title: MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection
Abstract:
arXiv:2604.27934v1 Announce Type: new 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 frame
Abstract:
arXiv:2604.27934v1 Announce Type: new 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 frame
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