Adversarial Information

Adversarial information research focuses on understanding and mitigating the impact of malicious or misleading information, particularly in the context of large language models and online environments. Current research explores methods to improve model robustness against adversarial attacks, including developing new metrics for evaluating model resilience and employing techniques like information bottleneck principles and adversarial training to enhance resistance to manipulation. This work is crucial for building more reliable AI systems and for combating the spread of misinformation, with implications for election integrity, public opinion, and the trustworthiness of online information.