6 papers · 1 filter
M4FC: a Multimodal, Multilingual, Multicultural, Multitask Real-World Fact-Checking Dataset
Jiahui Geng, Jonathan Tonglet, Iryna Gurevych
Existing real-world datasets for multimodal fact-checking have multiple limitations: they contain few instances, cover on only one or two languages, focus only on one task, or rely…
ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation
Jesus-German Ortiz-Barajas, Jonathan Tonglet, Vivek Gupta +1
Multimodal large language models (MLLMs) are increasingly used to automate chart generation from data tables, improving analysis and reporting efficiency while introducing new misu…
Is this chart lying to me? Automating the detection of misleading visualizations
Jonathan Tonglet, Jan Zimny, Tinne Tuytelaars +1
Misleading visualizations are a potent driver of misinformation on social media and the web. By violating chart design principles, they distort data and lead readers to draw inaccu…
Protecting multimodal large language models against misleading visualizations
Jonathan Tonglet, Tinne Tuytelaars, Marie-Francine Moens +1
Visualizations play a pivotal role in daily communication in an increasingly data-driven world. Research on multimodal large language models (MLLMs) for automated chart understandi…
NewsRECON: News article REtrieval for image CONtextualization
Jonathan Tonglet, Iryna Gurevych, Tinne Tuytelaars +1
Identifying when and where a news image was taken is crucial for journalists and forensic experts to produce credible stories and debunk misinformation. While many existing methods…
COVE: COntext and VEracity prediction for out-of-context images
Jonathan Tonglet, Gabriel Thiem, Iryna Gurevych
Images taken out of their context are the most prevalent form of multimodal misinformation. Debunking them requires (1) providing the true context of the image and (2) checking the…