Tag
DeLIVeR is a framework that uses a reinforced planner LLM to decompose claims into question sets for structured knowledge graph traversal, improving fact-checking accuracy over static RAG baselines by 10-15% on benchmark datasets.
This paper introduces a multi-dimensional taxonomy for characterizing cancer misinformation on Reddit, evaluating LLMs for annotation and finding that approximately 6% of cancer discussions contain misinformation. It identifies recurring narratives like unsupported treatments and distrust of conventional medicine.
ReMMD introduces a realistic multilingual multi-image agentic verification framework for multimodal misinformation detection, including a benchmark (ReMMDBench) with 500 samples and 2,756 images, and an agent (ReMMD-Agent) that achieves superior veracity performance with reduced costs.
Researchers from University of Technology Sydney compare fine-tuned transformers (DistilBERT, RoBERTa) against zero-shot LLMs (Llama variants, Claude, Gemini) for classifying misinformation responses on Reddit, finding that fine-tuned RoBERTa achieves 0.62 macro-F1 versus 0.50 for the best zero-shot model. The study shows that task-specific fine-tuning outperforms larger generalist models, particularly for detecting belief propagation, and that safety-alignment artifacts in frontier models can degrade performance.
This paper proposes a pipeline for fine-tuning LLMs specifically for explainable misinformation detection and introduces LonsRex, a data synthesis method to generate necessary and sufficient rationales, addressing limitations of naive filtering based solely on label correctness.
This paper introduces Misviz, a benchmark dataset of 2,604 real-world visualizations and 57,665 synthetic ones annotated with 12 types of misleading design violations, enabling automated detection of deceptive charts. The work evaluates state-of-the-art multimodal LLMs and rule-based systems on this challenging task, addressing the gap in resources for training AI models to combat data visualization misinformation.