Generate Hard to Detect Disinformation

Research on generating hard-to-detect disinformation focuses on understanding and mitigating the creation and spread of convincing false narratives, particularly leveraging the capabilities of large language models (LLMs). Current efforts concentrate on developing methods to identify LLM-generated disinformation, including novel datasets and algorithms like Siamese neural networks and transformer-based models, and exploring the effectiveness of various techniques such as sentiment analysis and stance detection for automated and human-in-the-loop detection. This work is crucial for safeguarding democratic processes and public discourse, as the ability to generate realistic disinformation poses a significant threat to information integrity and societal trust.