Rainbow Teaming

Rainbow teaming, and its evolution into Ruby and Violet teaming, are methodologies for improving the safety and robustness of large language models (LLMs). These approaches leverage adversarial techniques ("red teaming") to identify vulnerabilities, coupled with defensive strategies ("blue teaming") to mitigate them, often employing generative adversarial networks or similar frameworks to iteratively improve both attack and defense capabilities. This research aims to create more reliable and ethical AI systems by proactively addressing safety risks across diverse domains, such as safety, question answering, and cybersecurity, ultimately contributing to the development of more responsible and beneficial AI applications.