Factual News

Factual news research focuses on identifying and mitigating the spread of misinformation and bias in online news, aiming to improve the quality and trustworthiness of information available to the public. Current research employs large language models, particularly transformer-based architectures, for tasks such as bias detection, fact verification, and headline generation, often incorporating techniques like hierarchical prompting and attention mechanisms to enhance performance. These advancements are crucial for developing tools to improve news quality, combat the spread of fake news, and ultimately enhance the reliability of online information sources for both individuals and institutions.