Distraction Level

Distraction level research focuses on understanding and mitigating the negative impact of irrelevant information on the performance of various systems, particularly large language models and computer vision algorithms. Current research employs techniques like distraction-aware answer selection, bisimulation metrics for robust representation learning, and data augmentation strategies to improve model robustness against distractions. This work is significant because it addresses critical limitations in AI systems, improving their reliability and accuracy in real-world applications ranging from medical diagnosis and autonomous driving to human-computer interaction and navigation.