Robustness Issue

Robustness in machine learning, particularly deep learning, focuses on ensuring models maintain reliable performance under various conditions beyond those seen during training, addressing vulnerabilities to unexpected inputs or distributional shifts. Current research investigates robustness across diverse model architectures, including large language models and multimodal systems, employing techniques like adversarial training, data augmentation, and analysis of internal network behavior to identify and mitigate weaknesses. This research is crucial for deploying trustworthy AI systems in real-world applications, where unexpected inputs or environmental changes can lead to significant performance degradation or even catastrophic failures. A key challenge is developing comprehensive evaluation methods that accurately assess robustness across different types of perturbations and scenarios.