SNIFFER is a novel multimodal large language model specifically engineered for out-of-context misinformation detection and explanation, surpassing state-of-the-art methods in accuracy and providing precise explanations.
Using synthetic data generation for Out-Of-Context detection improves accuracy and reliability in identifying misinformation.
Multimodal large language model SNIFFER detects and explains out-of-context misinformation effectively.
SNIFFER is a novel multimodal large language model specifically engineered for detecting and explaining out-of-context misinformation.
This research paper introduces Misinformation with Legal Consequences (MisLC), a new task leveraging large language models to detect misinformation that could potentially violate existing laws.
Deep learning models, particularly hybrid CNN+LSTM architectures, outperform conventional machine learning classifiers in detecting COVID-19 misinformation on social media.
Combining large language models (LLMs) with web retrieval agents significantly improves the accuracy of misinformation detection, outperforming LLMs used in isolation.