arXiv:2605.27449v1 Announce Type: cross Abstract: In the field of multimodal fact checking, the accuracy of retrieving evidence from different modalities has a significant impact on the downstream claim verification process. Existing general multimodal retrieval methods are often constructed based on semantics, resulting in the retrieved evidence being similar but not relevant to the claim. This paper proposes a \textbf{D}ynamic \textbf{A}daptive \textbf{C}ontrastive \textbf{L}earning method for evidence \textbf{R}etrieval called DACLR to address these issues. DACLR first uses a Multimodal Lar
The rapid proliferation of multimodal information and sophisticated disinformation campaigns necessitates more robust fact-checking mechanisms, driving demand for advanced evidence retrieval systems.
Improved evidence retrieval directly enhances the accuracy of AI systems in fact-checking, which is crucial for maintaining trust in information and the reliability of AI-driven analysis.
Multimodal fact-checking systems can now leverage more relevant, rather than just semantically similar, evidence, leading to more accurate claim verification.
- · Fact-checking organizations
- · AI developers
- · Information consumers
- · News media
- · Propagandists
- · Disinformation networks
- · Systems reliant on semantic-only retrieval
Increased accuracy and efficiency in AI-driven fact-checking and content moderation platforms.
Reduced prevalence of successfully spread misinformation due to more effective automated detection.
Potentially, a more resilient information ecosystem and increased public trust in AI-verified content.
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Read at arXiv cs.AI