MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models

This research paper from the University of Central Florida introduces MM-SafetyBench, a comprehensive benchmark designed specifically for evaluating the safety aspects of Multimodal Large Language Models (MLLMs). Explore how this innovative framework addresses the critical need for standardized safety testing in systems that process both text and visual inputs, examining potential vulnerabilities and harmful outputs across various risk categories. Learn about the methodology, evaluation metrics, and findings that help identify safety gaps in current multimodal AI systems, providing valuable insights for researchers and developers working to create more responsible and trustworthy multimodal technologies.