Safety Evaluation Benchmark

Safety evaluation benchmarks for large language models (LLMs) and vision LLMs aim to systematically assess and quantify the risks associated with these powerful AI systems. Current research focuses on developing comprehensive benchmarks that incorporate diverse safety concerns, including adversarial attacks and out-of-distribution inputs, often employing techniques like automated test generation and risk scoring using LLMs themselves. These benchmarks are crucial for improving the safety and reliability of LLMs, informing model development, and ultimately mitigating potential harms in real-world applications. Furthermore, similar benchmarks are being developed for other AI systems, such as autonomous vehicles, to rigorously evaluate their safety performance.