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arXiv:2604.04692v3 Announce Type: replace-cross Abstract: Automated fact-checking is a crucial task that supports a responsible information ecosystem. While recent research has progressed from text-o...
arXiv:2606. 24112v1 Announce Type: new Abstract: Multimodal misinformation detection is increasingly important because viral posts now combine long multilingual narratives, several images, mixed provenance, and subtle text--image framing errors.
arXiv:2610.28087v1 Announce Type: new Abstract: AI-generated images linked to news events and online claims can mislead audiences, enable fraud, or cause public controversy. We introduce WildAIGC-Ben...
AfrIFact is a new dataset designed to support automatic fact‑checking in ten African languages and English, covering information retrieval, evidence extraction, and fact verification. Experiments show that current embedding models struggle with cross‑lingual retrieval, and that cultural and news documents are easier to retrieve than healthcare documents. Large language models also lack robust multilingual fact‑verification, but few‑shot prompting and task‑specific fine‑tuning can significantly improve performance.
The paper introduces a new perspective on entity rarity in multimodal entity linking by using knowledge‑graph structural metrics instead of popularity metrics, revealing many rare entities previously overlooked. Experiments show that state‑of‑the‑art models suffer a 15.4–39.9% accuracy drop on these rare‑entity slices. The authors propose a training‑free framework that combines reasoning and retrieval with a vision‑language model, achieving a 6.9% overall accuracy gain and up to 23.3% improvement on rare entities, and release a new benchmark MERLIN‑Rare for focused evaluation.
arXiv:2607. 12375v1 Announce Type: cross Abstract: Image Quality Assessment (IQA) in open-world environments remains challenging due to limited generalization and interpretability.