Analyzing Out-of-Context Misinformation Detection with SNIFFER
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.
Detection of Out-Of-Context Misinformation Using Synthetic Multimodal Data
Using synthetic data generation for Out-Of-Context detection improves accuracy and reliability in identifying misinformation.
SNIFFER: Multimodal Large Language Model for Out-of-Context Misinformation Detection
Multimodal large language model SNIFFER detects and explains out-of-context misinformation effectively.
SNIFFER: Multimodal Large Language Model for Out-of-Context Misinformation Detection
SNIFFER is a novel multimodal large language model specifically engineered for detecting and explaining out-of-context misinformation.
Detecting Misinformation with Legal Consequences (MisLC): A Novel Task Using Large Language Models
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.
A Comparative Study of Machine Learning and Deep Learning Models for Detecting COVID-19 Misinformation in Text
Deep learning models, particularly hybrid CNN+LSTM architectures, outperform conventional machine learning classifiers in detecting COVID-19 misinformation on social media.
Web Retrieval Agents Enhance Large Language Model Performance in Misinformation Detection
Combining large language models (LLMs) with web retrieval agents significantly improves the accuracy of misinformation detection, outperforming LLMs used in isolation.
© 2024 by Linnk AI